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LifeLogger wearable camera spots faces, speech & text: Hands-on

Anybody can clip on a camera and call it a life-logger, but startup LifeLogger says its wearable goes the extra mile with its combination of face, text, and even audio recognition to make reviewing your “augmented memory” more meaningful. Showing at CES 2014 this week, LifeLogger’s approach consists of a tiny, gum-packet sized stick camera weighing around 9g and which can record 720/30p HD video as well as stills, and a companion cloud service that does the heavy lifting by processing all that recorded content and allowing you to make better sense of it. We grabbed some hands-on time at the show to find out more.

The LifeLogger camera itself is relatively discrete, finished in two-tone grey plastic with a pair of silver buttons on the side: one for triggering video and the other for taking a photo. It slots onto a black plastic band which runs around the back of the head, and which keeps it in place against the side of your face. It’s not the most handsome of solutions, but it’s probably a little more subdued than Google’s Glass.

Unlike, say, Narrative’s Clip, LifeLogger’s camera isn’t necessarily intended to record all day. Although you can leave it in persistent record mode – to either 32GB or 64GB of internal storage – the battery life in that case will be just a couple of hours. More likely is that wearers will trigger the camera as they move around, hitting the video record button when they spot something or someone they want to later remember, though the company will offer a “BaseStation” with an integrated battery for extended runtimes and continuous live-streaming.

What differentiates LifeLogger’s system from that of others is the cloud-side processing. There probably aren’t enough hours in our lives to review each and every full day’s worth of logged video and photos, so the startup turns to automated systems to help pick out the important parts of each segment.

First, there’s location, using the GPS built into the camera. That shows where you were during each moment of the video, as well as which way you were facing, allowing you to jump to a specific spot for review. Cleverer, though, is LifeLogger’s use of OCR, face, and audio recognition.

A similar thing happens with audio in the clip, LifeLogger’s software identifying snippets of speech and allowing you to go straight to the part of the recording when it was heard. Finally, there’s face recognition, with a slideshow of faces spotted in the video that can be similarly reviewed; if you take the time to tag each face with a name – such as those of your friends and family – then the software will allow you to see all instances of them across every recording you’ve made.

It’s an ambitious project, certainly, but this sort of analysis is vital if life-logging as a whole is going to take off as more than a niche obsession. Few people have time to go through even all of the photos they’re taking with their smartphones and cameras; add in the exponentially greater amount of content produced when you’re recording 24/7, and it would quickly be a route to media overload.

We’ll have to spend some proper time with LifeLogger’s system to see just how well it works out in the wild, with the camera set to go on sale around June. There’s no final price yet, though company president Stew Garner says it’s expected to be somewhere in the $200-250 bracket, with some sort of monthly or yearly fee for the online analysis and management component.

You're reading Lifelogger Wearable Camera Spots Faces, Speech & Text: Hands

Speech Emotion Recognition (Ser) Through Machine Learning

Authors: Mohit Wadhwa,  Anurag  Gupta, Prateek Kumar Pandey

Acknowledgements: Paulami Das, Head of Data Science CoE, and Anish Roychowdhury,  Senior Analytics Leader, Brillio

Organizations: Brillio Technologies, Indian Institute of Technology, Kharagpur

1. Background

As human beings speech is amongst the most natural way to express ourselves. We depend so much on it that we recognize its importance when resorting to other communication forms like emails and text messages where we often use emojis to express the emotions associated with the messages. As emotions play a vital role in communication, the detection and analysis of the same is of vital importance in today’s digital world of remote communication. Emotion detection is a challenging task, because emotions are subjective. There is no common consensus on how to measure or categorize them. We define a SER system as a collection of methodologies that process and classify speech signals to detect emotions embedded in them. Such a system can find use in a wide variety of application areas like interactive voice based-assistant or caller-agent conversation analysis. In this study we attempt to detect underlying emotions in recorded speech by analysing the acoustic features of the audio data of recordings.  

2. Solution Overview

There are three classes of features in a speech namely, the lexical features (the vocabulary used), the visual features (the expressions the speaker makes) and the acoustic features (sound properties like pitch, tone, jitter, etc.). The problem of speech emotion recognition can be solved by analysing one or more of these features. Choosing to follow the lexical features would require a transcript of the speech which would further require an additional step of text extraction from speech if one wants to predict emotions from real-time audio. Similarly, going forward with analysing visual features would require the excess to the video of the conversations which might not be feasible in every case while the analysis on the acoustic features can be done in real-time while the conversation is taking place as we’d just need the audio data for accomplishing our task. Hence, we choose to analyse the acoustic features in this work. Furthermore, the representation of emotions can be done in two ways:

Discrete Classification: Classifying emotions in discrete labels like anger, happiness, boredom, etc.

Dimensional Representation: Representing emotions with dimensions such as Valence (on a negative to positive scale), Activation or Energy (on a low to high scale) and Dominance (on an active to passive scale)

Both these approaches have their pros and cons. The dimensional approach is more elaborate and gives more context to prediction but it is harder to implement and there is a lack of annotated audio data in a dimensional format. The discrete classification is more straightforward and easier to implement but it lacks the context of the prediction that dimensional representation provides. We have used the discrete classification approach in the current study for lack of dimensionally annotated data in the public domain.  

3. Data Sources

The data used in this project was combined from five different data sources as mentioned below:

TESS (Toronto Emotional Speech Set): 2 female speakers (young and old), 2800 audio files, random words were spoken in 7 different emotions.

SAVEE (Surrey Audio-Visual Expressed Emotion): 4 male speakers, 480 audio files, same sentences were spoken in 7 different emotions.

RAVDESS: 2452 audio files, with 12 male speakers and 12 Female speakers, the lexical features (vocabulary) of the utterances are kept constant by speaking only 2 statements of equal lengths in 8 different emotions by all speakers.

CREMA-D (Crowd-Sourced Emotional Multimodal Actors Dataset): 7442 audio files, 91 different speakers (48 male and 43 female between the ages of 20 and 74) of different races and ethnicities, different statements are spoken in 6 different emotions and 4 emotional levels (low, mid, high and unspecified).

Berlin: 5 male and 5 female speakers, 535 audio files, 10 different sentences were spoken in 6 different emotions.

4. Features used in this study

From the Audio data we have extracted three key features which have been used in this study, namely, MFCC (Mel Frequency Cepstral Coefficients), Mel Spectrogram and Chroma. The Python implementation of Librosa package was used in their extraction.

Choice of features

MFCC was by far the most researched about and utilized features in research papers and open source projects.

Mel spectrogram plots amplitude on frequency vs time graph on a “Mel” scale. As the project is on emotion recognition, a purely subjective item, we found it better to plot the amplitude on Mel scale as Mel scale changes the recorded frequency to “perceived frequency”.

Researchers have also used Chroma in their projects as per literatures, thus we also tried basic modeling with only MFCC and Mel and with all MFCC, Mel, Chroma. The model with all of the features gave slightly better results, hence we chose to keep all three features

  Details about the features are mentioned below.  

MFCC (Mel Frequency Cepstral Coefficients)

In the conventional analysis of time signals, any periodic component (for example, echoes) shows up as sharp peaks in the corresponding frequency spectrum (i.e. Fourier spectrum. This is obtained by applying a Fourier transform on the time signal). Any cepstrum feature is obtained by applying Fourier Transform on a spectrogram. The special characteristic of MFCC is that it is taken on a Mel scale which is a scale that relates the perceived frequency of a tone to the actual measured frequency. It scales the frequency in order to match more closely what the human ear can hear. The envelope of the temporal power spectrum of the speech signal is representative of the vocal tract and MFCC accurately represents this envelope.

Mel Spectrogram

A Fast Fourier Transform is computed on overlapping windowed segments of the signal, and we get what is called the spectrogram. This is just a spectrogram that depicts amplitude which is mapped on a Mel scale.

Chroma

A Chroma vector is typically a 12-element feature vector indicating how much energy of each pitch class is present in the signal in a standard chromatic scale.  

5. Pre Processing

As  the typical output of the feature extracted were 2D in form, we decided to take a bi-directional approach using both a 1D form of input and a 2D form of input as discussed below

1D Data Format

These features obtained from extraction from audio clips are in a matrix format. To model them on traditional ML algorithms like SVM and XGBoost or on 1-D CNN, we considered converting the matrices into the 1-D format by taking row means and column means. Upon preliminary modelling the results obtained from the array of row means turned out to be better than the array of column means, so we proceeded with the 1-D array obtained from row means of the feature matrices.

2D Data Format

The 2D features were used in the deep learning model (CNN). The y-axis of the feature matrices obtained depends on the n_mfcc or n_mels parameter we choose while extracting data. The x-axis depends upon the audio duration and the sampling rate we choose while feature extraction. Since the audio clips in our datasets were of varying lengths ranging from just under 2 seconds to over 6 seconds, steps like choosing one median length where we’ll clip all audio files and pad all shorter files with zeroes to maintain dimensions wouldn’t be feasible. This is because this would have resulted in the loss of information for longer clips and the shorter clips would be just silence for the latter half of their audio length. To check this problem, we decided to use different sampling rates in extraction in accordance with their audio lengths. In our approach any, audio file greater or equal to 5 seconds was clipped at 5 seconds and sampled at 16000 Hz and the shorter clips were sampled such that the audio duration * sampling rate multiple remains 80000. In this way, we were able to maintain the dimensions of the matrix for all audio clips without losing much of the information.  

6. Exploratory Data Analysis

The combined data set from the original 5 sources is thoroughly analysed with respect to the following aspects

Emotion distribution by gender

Variation in energy across emotions

Variation of relative pace and power across emotions

We checked the distribution of labels with respect to emotions and gender and found that while the data is balanced for six emotions viz. neutral, happy, sad, angry, fear and disgust, the number of labels was slightly less for surprise and negligible for boredom. While the slightly fewer instances of surprise can be overlooked on account of it being a rarer emotion, the imbalance against boredom was rectified later by clubbing sadness and boredom together due to them being similar acoustically. It’s also worth noting that boredom could have been combined with neutral emotion but since both sadness and boredom are negative emotions, it made more sense to combine them.

Emotion Distribution of Gender

Regarding the distribution of gender, the number of female speakers was found to be slightly more than the male speakers, but the imbalance was not large enough to warrant any special attention. Refer Figure. 1

Fig.1   Distributions of emotion with respect to gender  

Variation in Energy Across  Emotions

To ensure uniformity in our study of energy variation as the audio clips in our dataset were of different lengths, a power which is energy per unit time was found to be a more accurate measure. This metric was plotted with respect to different emotions. From the graph See Fig. 2)  it is quite evident that the primary method of expression of anger or fear in people is a higher energy delivery. We also observe that disgust and sadness are closer to neutral with regards to energy although exceptions do exist.

Figure 2   Distributions of emotion with respect to gender  

Variation of Relative Pace and Power with respect to Emotions

A scatter-plot of power vs relative pace of the audio clips was analysed and it was observed that the ‘disgust’ emotion was skewed towards the low pace side while the ‘surprise’ emotion was skewed more towards the higher pace side. As mentioned before, anger and fear occupy the high power space and sadness and neutral occupy the low power space while being scattered pace-wise. Only, the RAVDESS dataset was used for plotting here because it contains only two sentences of equal length spoken in different emotions, so the lexical features don’t vary and the relative pace can be reliably calculated.

 

Figure 3 Scatter of power Vs relative pace of audio clips  

7. Modelling Solution Pipeline

The solution pipeline for this study is depicted in the schematic shown in Fig. 4.  The raw signal is the input which is processed as shown. At first the 2D features were extracted from the datasets and converted into 1-D form by taking the row means.  A measure of noise was added to the raw audio for 4 of our datasets (except CREMA-D as the others were studio recording and thus cleaner). The features were then extracted from those noisy files and our dataset was augmented with them. Post feature extraction we applied various ML algorithms such as SVM, XGB, CNN-1D(Shallow) and CNN-1D on our 1D data frame and CNN-2D on our 2D-tensor. As some of the models were overfitting the data, and taking into consideration a large number of features (181 in 1D) we tried dimensionality reduction to check overfitting and trained the models again.

Figure 4: Schematic of solution pipeline  

Selection of Train Test Data

We chose a customized split logic for the various ML models used. For the SVM and XGB models the model was simply split into train-test data in the ratio of 80:20 and validated using cross-validation with 5-folds. For CNN both 1D and 2D, train-test-split was used consecutively,  such that at first the data was split in 90:10 ratio where the 10% was test set, the remaining 90% was again split in 80:20 ratio of train and validation set.  

CNN Model Architectures CNN-1D (shallow)

This model consisted of 1 Convolution layer of 64 channels and same padding followed by a dense layer and the output layer.

CNN-2D (deep)

This model was constructed in a similar format as VGG-16, but the last 2 blocks of 3 convolution layers were removed to reduce complexity. This CNN model had the following architectural complexity:

2 convolution layers of 64 channels, 3×3 kernel size and same padding followed by a max-pooling layer of size 2×2 and stride 2×2.

2 convolution layers of 128 channels, 3×3 kernel size and same padding followed by a max-pooling layer of size 2×2 and stride 2×2.

3 convolution layers of 256 channels, 3×3 kernel size and same padding followed by a max-pooling layer of size 2×2 and stride 2×2.

Each convolution layer had the ‘relu’ activation function.

After flattening, two dense layers of 512 units each were added and dropout layers of 0.1 and 0.2 were added after each dense layer.

Finally, the output layer was added with a ‘softmax’ activation function.

Model Results Comparison

The result is based on the accuracy metrics in which there is a comparison between predicted values and the actual values. A confusion matrix is created which consists of true positive (TP), true negative (TN), false positive (FP), and false negative (FN). From confusion metrics, we have calculated accuracy as follows:

The model was trained on training data and tested on test data with different numbers of epochs starting from 50 to 100, 150 and 200. The accuracies were compared among all models viz. SVM, XGBoost and Convolution Neural Network (shallow and deep) for 1D features and 2D features.  Fig. 5  shows the comparative performance across different models.  

Figure 5 : Comparative Results from different models We find from Fig.5  that though the CNN-1D Deep model gave the best accuracy on the test set, CNN-1D Deep and CNN-2D models are clearly overfitting the dataset with their training accuracy being is 99% and 98.38 % respectively against a much lower test and validation accuracies. On the other hand, CNN-1D Shallow gave much better results on account of it being more stable with its train, validation and test accuracies being closer to each other, though its testing accuracy was a little lower than the CNN-1D.  

Dimensionality Reduction Approach

In order to rectify the overfitting of the models we used a dimensionality reduction approach. PCA technique was employed for dimensionality reduction in 1D features and dimensions were reduced from 180 to 120 with an explained variance of 98.3%. Dimensionality reduction made the model slightly less accurate but reduced the training time, however it didn’t do much to reduce overfitting in the deep learning model. From this we deduced that our dataset is simply not big enough for a complex model to perform well and realised the solution was limited by lack of a larger data volume.  Fig. 6 summarizes the results for different models post dimensionality reduction.

Figure 6 : Comparative Results from different models after PCA based dimensionality reduction

Insights from Testing User Recordings

We tested the developed models on user recordings, from the test results we have the following observations

An ensemble of CNN-2D and CNN-1D (shallow and deep) based on a soft voting gave best results on user recordings.

The model often got confused between anger and disgust.

The model also got confused among low energy emotions which are sadness, boredom and neutral.

If one or two words are spoken in higher volume than other words, especially at start or end of a sentence, it almost always classifies as fear or surprise.

The model seldom classifies an emotion as happy.

The model isn’t too noise sensitive, meaning it doesn’t falter as long as background noise is not too high.

Grouping Similar Emotions

Since the model was confusing between similar emotions like anger-disgust and sad-bored, we tried combining those labels and training the model on 6 classes which were neutral, sadness/boredom, happy, anger/disgust, surprise and fear. The accuracies certainly improved on reducing the number of classes, but this introduced another problem with regards to class imbalance. After, combining anger-disgust and sad-boredom, the model developed a high bias towards the anger-disgust. This may have happened because the number of instances of anger-disgust became disproportionately more than the other labels. So, it was decided to stick with the older model.  

Final Prediction Pipeline

The final prediction pipeline is depicted schematically in Fig. 7 below.

Figure 7  Final prediction pipeline  

8. Conclusions and Future Scope

Through this project, we showed how we can leverage Machine learning to obtain the underlying emotion from speech audio data and some insights on the human expression of emotion through voice. This system can be employed in a variety of setups like Call Centre for complaints or marketing, in voice-based virtual assistants or chatbots, in linguistic research, etc. A few possible steps that can be implemented to make the models more robust and accurate are the following

An accurate implementation of the pace of the speaking can be explored to check if it can resolve some of the deficiencies of the model.

Figuring out a way to clear random silence from the audio clip.

Exploring other acoustic features of sound data to check their applicability in the domain of speech emotion recognition. These features could simply be some proposed extensions of MFCC like RAS-MFCC or they could be other features entirely like LPCC, PLP or Harmonic cepstrum.

Following lexical features based approach towards SER and using an ensemble of the lexical and acoustic models. This will improve the accuracy of the system because in some cases the expression of emotion is contextual rather than vocal.

Adding more data volume either by other augmentation techniques like time-shifting or speeding up/slowing down the audio or simply finding more annotated audio clips.

9. Acknowledgements

The authors wish to express their gratitude to Paulami Das, Head of Data Science CoE @ Brillio and Anish Roychowdhury,  Senior Analytics Leader @ Brillio for their mentoring and guidance towards shaping up this study.  

-REFERENCES- Blogs and Documentations: MFCC Dataset References: Literature References

Kernel References Authors:

Prateek Kumar Pandey Prateek Kumar Pandey is an Undergraduate enrolled in IIT Kharagpur pursuing a Dual Degree (BTech.+MTech.) in the department of Civil Engineering and is currently in his Fourth year. He has worked as a Data Science Intern for Brillio where he worked on a Speech Emotion Recognition project. He has proficiency in Python programming and has previously worked on projects in areas of NLP and Time Series forecasting. Anurag Gupta Anurag Gupta is a pre-final year undergraduate student enrolled in Dual Degree (B.Tech+M.Tech) course in the Department of Civil Engineering, IIT Kharagpur.  He is interested in the field of Data Science, Natural Language Processing, Predictive Data Modelling and solving the real-world problems with the help of these technologies. He likes to play with data and bring valuable insights and results out of them whether it comes to structured or unstructured data. He is keen to work on the projects that can help him in nurturing skills and knowledge. Previously he has worked in BRILLIO as Data Science Intern where he worked on machine learning project based on Speech Emotion Recognition. He has worked at IISC Bangalore on a real-life project for forecasting Transit Ridership of BMTC buses. Mohit Wadhwa

As human beings speech is amongst the most natural way to express ourselves. We depend so much on it that we recognize its importance when resorting to other communication forms like emails and text messages where we often use emojis to express the emotions associated with the messages. As emotions play a vital role in communication, the detection and analysis of the same is of vital importance in today’s digital world of remote communication. Emotion detection is a challenging task, because emotions are subjective. There is no common consensus on how to measure or categorize them. We define a SER system as a collection of methodologies that process and classify speech signals to detect emotions embedded in them. Such a system can find use in a wide variety of application areas like interactive voice based-assistant or caller-agent conversation analysis. In this study we attempt to detect underlying emotions in recorded speech by analysing the acoustic features of the audio data of recordings.There are three classes of features in a speech namely, the lexical features (the vocabulary used), the visual features (the expressions the speaker makes) and the acoustic features (sound properties like pitch, tone, jitter, etc.). The problem of speech emotion recognition can be solved by analysing one or more of these features. Choosing to follow the lexical features would require a transcript of the speech which would further require an additional step of text extraction from speech if one wants to predict emotions from real-time audio. Similarly, going forward with analysing visual features would require the excess to the video of the conversations which might not be feasible in every case while the analysis on the acoustic features can be done in real-time while the conversation is taking place as we’d just need the audio data for accomplishing our task. Hence, we choose to analyse the acoustic features in this work. Furthermore, the representation of emotions can be done in two ways:Both these approaches have their pros and cons. The dimensional approach is more elaborate and gives more context to prediction but it is harder to implement and there is a lack of annotated audio data in a dimensional format. The discrete classification is more straightforward and easier to implement but it lacks the context of the prediction that dimensional representation provides. We have used the discrete classification approach in the current study for lack of dimensionally annotated data in the public chúng tôi data used in this project was combined from five different data sources as mentioned below:From the Audio data we have extracted three key features which have been used in this study, namely, MFCC (Mel Frequency Cepstral Coefficients), Mel Spectrogram and Chroma. The Python implementation of Librosa package was used in their chúng tôi the conventional analysis of time signals, any periodic component (for example, echoes) shows up as sharp peaks in the corresponding frequency spectrum (i.e. Fourier spectrum. This is obtained by applying a Fourier transform on the time signal). Any cepstrum feature is obtained by applying Fourier Transform on a spectrogram. The special characteristic of MFCC is that it is taken on a Mel scale which is a scale that relates the perceived frequency of a tone to the actual measured frequency. It scales the frequency in order to match more closely what the human ear can hear. The envelope of the temporal power spectrum of the speech signal is representative of the vocal tract and MFCC accurately represents this envelope.A Fast Fourier Transform is computed on overlapping windowed segments of the signal, and we get what is called the spectrogram. This is just a spectrogram that depicts amplitude which is mapped on a Mel scale.A Chroma vector is typically a 12-element feature vector indicating how much energy of each pitch class is present in the signal in a standard chromatic chúng tôi the typical output of the feature extracted were 2D in form, we decided to take a bi-directional approach using both a 1D form of input and a 2D form of input as discussed belowThese features obtained from extraction from audio clips are in a matrix format. To model them on traditional ML algorithms like SVM and XGBoost or on 1-D CNN, we considered converting the matrices into the 1-D format by taking row means and column means. Upon preliminary modelling the results obtained from the array of row means turned out to be better than the array of column means, so we proceeded with the 1-D array obtained from row means of the feature chúng tôi 2D features were used in the deep learning model (CNN). The y-axis of the feature matrices obtained depends on the n_mfcc or n_mels parameter we choose while extracting data. The x-axis depends upon the audio duration and the sampling rate we choose while feature extraction. Since the audio clips in our datasets were of varying lengths ranging from just under 2 seconds to over 6 seconds, steps like choosing one median length where we’ll clip all audio files and pad all shorter files with zeroes to maintain dimensions wouldn’t be feasible. This is because this would have resulted in the loss of information for longer clips and the shorter clips would be just silence for the latter half of their audio length. To check this problem, we decided to use different sampling rates in extraction in accordance with their audio lengths. In our approach any, audio file greater or equal to 5 seconds was clipped at 5 seconds and sampled at 16000 Hz and the shorter clips were sampled such that the audio duration * sampling rate multiple remains 80000. In this way, we were able to maintain the dimensions of the matrix for all audio clips without losing much of the chúng tôi combined data set from the original 5 sources is thoroughly analysed with respect to the following aspectsWe checked the distribution of labels with respect to emotions and gender and found that while the data is balanced for six emotions vizand, the number of labels was slightly less forand negligible for. While the slightly fewer instances of surprise can be overlooked on account of it being a rarer emotion, the imbalance against boredom was rectified later by clubbing sadness and boredom together due to them being similar acoustically. It’s also worth noting that boredom could have been combined with neutral emotion but since bothandare negative emotions, it made more sense to combine them.Regarding the distribution of gender, the number of female speakers was found to be slightly more than the male speakers, but the imbalance was not large enough to warrant any special attention. Refer Figure. 1Fig.1 Distributions of emotion with respect to genderTo ensure uniformity in our study of energy variation as the audio clips in our dataset were of different lengths, a power which is energy per unit time was found to be a more accurate measure. This metric was plotted with respect to different emotions. From the graph See Fig. 2) it is quite evident that the primary method of expression of anger or fear in people is a higher energy delivery. We also observe that disgust and sadness are closer to neutral with regards to energy although exceptions do exist.Figure 2 Distributions of emotion with respect to genderA scatter-plot of power vs relative pace of the audio clips was analysed and it was observed that the ‘disgust’ emotion was skewed towards the low pace side while the ‘surprise’ emotion was skewed more towards the higher pace side. As mentioned before, anger and fear occupy the high power space and sadness and neutral occupy the low power space while being scattered pace-wise. Only, the RAVDESS dataset was used for plotting here because it contains only two sentences of equal length spoken in different emotions, so the lexical features don’t vary and the relative pace can be reliably calculated.Figure 3 Scatter of power Vs relative pace of audio clipsThe solution pipeline for this study is depicted in the schematic shown in Fig. 4. The raw signal is the input which is processed as shown. At first the 2D features were extracted from the datasets and converted into 1-D form by taking the row means. A measure of noise was added to the raw audio for 4 of our datasets (except CREMA-D as the others were studio recording and thus cleaner). The features were then extracted from those noisy files and our dataset was augmented with them. Post feature extraction we applied various ML algorithms such as SVM, XGB, CNN-1D(Shallow) and CNN-1D on our 1D data frame and CNN-2D on our 2D-tensor. As some of the models were overfitting the data, and taking into consideration a large number of features (181 in 1D) we tried dimensionality reduction to check overfitting and trained the models again.Figure 4: Schematic of solution pipelineWe chose a customized split logic for the various ML models used. For the SVM and XGB models the model was simply split into train-test data in the ratio of 80:20 and validated using cross-validation with 5-folds. For CNN both 1D and 2D, train-test-split was used consecutively, such that at first the data was split in 90:10 ratio where the 10% was test set, the remaining 90% was again split in 80:20 ratio of train and validation chúng tôi model consisted of 1 Convolution layer of 64 channels and same padding followed by a dense layer and the output chúng tôi model was constructed in a similar format as VGG-16, but the last 2 blocks of 3 convolution layers were removed to reduce complexity. This CNN model had the following architectural complexity:The result is based on the accuracy metrics in which there is a comparison between predicted values and the actual values. A confusion matrix is created which consists of true positive (TP), true negative (TN), false positive (FP), and false negative (FN). From confusion metrics, we have calculated accuracy as follows:The model was trained on training data and tested on test data with different numbers of epochs starting from 50 to 100, 150 and 200. The accuracies were compared among all models viz. SVM, XGBoost and Convolution Neural Network (shallow and deep) for 1D features and 2D features. Fig. 5 shows the comparative performance across different models.Figure 5 : Comparative Results from different models We find from Fig.5 that though the CNN-1D Deep model gave the best accuracy on the test set, CNN-1D Deep and CNN-2D models are clearly overfitting the dataset with their training accuracy being is 99% and 98.38 % respectively against a much lower test and validation accuracies. On the other hand, CNN-1D Shallow gave much better results on account of it being more stable with its train, validation and test accuracies being closer to each other, though its testing accuracy was a little lower than the chúng tôi order to rectify the overfitting of the models we used a dimensionality reduction approach. PCA technique was employed for dimensionality reduction in 1D features and dimensions were reduced from 180 to 120 with an explained variance of 98.3%. Dimensionality reduction made the model slightly less accurate but reduced the training time, however it didn’t do much to reduce overfitting in the deep learning model. From this we deduced that our dataset is simply not big enough for a complex model to perform well and realised the solution was limited by lack of a larger data volume. Fig. 6 summarizes the results for different models post dimensionality reduction.Figure 6 : Comparative Results from different models after PCA based dimensionality reductionWe tested the developed models on user recordings, from the test results we have the following observationsSince the model was confusing between similar emotions like anger-disgust and sad-bored, we tried combining those labels and training the model on 6 classes which were neutral, sadness/boredom, happy, anger/disgust, surprise and fear. The accuracies certainly improved on reducing the number of classes, but this introduced another problem with regards to class imbalance. After, combining anger-disgust and sad-boredom, the model developed a high bias towards the anger-disgust. This may have happened because the number of instances of anger-disgust became disproportionately more than the other labels. So, it was decided to stick with the older chúng tôi final prediction pipeline is depicted schematically in Fig. 7 below.Figure 7 Final prediction pipelineThrough this project, we showed how we can leverage Machine learning to obtain the underlying emotion from speech audio data and some insights on the human expression of emotion through voice. This system can be employed in a variety of setups like Call Centre for complaints or marketing, in voice-based virtual assistants or chatbots, in linguistic research, chúng tôi authors wish to express their gratitude to Paulami Das, Head of Data Science CoE @ Brillio and Anish Roychowdhury, Senior Analytics Leader @ Brillio for their mentoring and guidance towards shaping up this study.Prateek Kumar Pandey is an Undergraduate enrolled in IIT Kharagpur pursuing a Dual Degree (BTech.+MTech.) in the department of Civil Engineering and is currently in his Fourth year. He has worked as a Data Science Intern for Brillio where he worked on a Speech Emotion Recognition project. He has proficiency in Python programming and has previously worked on projects in areas of NLP and Time Series forecasting.Anurag Gupta is a pre-final year undergraduate student enrolled in Dual Degree (B.Tech+M.Tech) course in the Department of Civil Engineering, IIT Kharagpur. He is interested in the field of Data Science, Natural Language Processing, Predictive Data Modelling and solving the real-world problems with the help of these technologies. He likes to play with data and bring valuable insights and results out of them whether it comes to structured or unstructured data. He is keen to work on the projects that can help him in nurturing skills and knowledge. Previously he has worked in BRILLIO as Data Science Intern where he worked on machine learning project based on Speech Emotion Recognition. He has worked at IISC Bangalore on a real-life project for forecasting Transit Ridership of BMTC buses.Mohit Washwa is currently a Data Scientist in Brillio. He has a total of 3+ years of Industry Experience. Prior to this, he was part of AI solutions team in Infosys. He has worked on projects based on Machine Learning, Deep Learning and Computer Vision. His skills helped some clients in achieving Code Optimization around concepts such as Genetic Algorithm. He has leveraged his understanding of object detection algorithms like Yolo by delivering an interesting computer vision solution that demonstrate how customers in a retail store can be automatically billed for items picked up from shelves. This solution was presented at NRF (National Retail Federation) in New York. He has experience of working on Demand forecasting projects that aims to predict the demand of a SKU and has delivered some other client solutions involving deep learning algorithms such as CNN, RNN, LSTM. He holds a good hand on Python and has pursued BTech. in Computer Engineering from Punjabi University, Patiala.

Microsoft Band Vs The Wearable Competition

Microsoft Band vs the Wearable Competition

You’d need a very big wrist to wear this year’s crop of fitness bands and smartwatches, but Microsoft believes the new Microsoft Band can elbow out the competition. Straddling the line between smartwatch and health tracker – not to mention spanning not only Windows Phone but iPhone and Android, in a play for cross-compatibility that rivals could learn a lesson from – the sensor-packed wearable claims to deliver the best of both worlds. In the process, though, Microsoft has arguably given itself double the challenge, so I pulled up my sleeves to see how the Microsoft Band holds up.

On the smartwatch side, you get a smaller display but an arguably smaller form-factor. Microsoft’s play for compactness isn’t entirely successful, though, given the chunkiness of its strap; as we noted yesterday, all those sensors have to go somewhere.

Nor is it necessarily less discrete, particularly when Motorola’s Moto 360 has its traditionally-styled round display.

Battery life promises to be more akin to a smartwatch than a fitness band, too: Microsoft is saying two days of use, versus the roughly day-long runtimes of the Android Wear crowd (and what Apple is warning us to expect from the Apple Watch). You’ll get more runtime out of a Pebble or something based on Qualcomm’s Toq platform, such as the new Timex GPS One+; then again, you don’t get a color touchscreen with Pebble, and the mirasol panel on the Timex has muted colors and a dearth of third-party app support.

That app range is going to become increasingly important as third-party developers weigh in. There’s a fairly broad selection for Pebble, but the aging hardware represents the most significant limitation. Developers simply don’t get the system grunt that they do with, say, Android Wear.

That’s not to say Android Wear users have a vast choice either, of course. The Play Store is gaining a gradual trickle of titles, certainly, but it’s far from a gush, and some of the momentum has been lost as developers get to grips with both square and circular screens. Google’s latest Android Wear update arguably hasn’t helped, either, enabling GPS support which is something the first generation of hardware doesn’t actually come equipped with.

It’s too soon to say how Apple Watch will fare for apps, though given Apple’s strategy of teasing iOS coders with its early preview this year, and past evidence for other platform debuts like iPad, I’d expect a fair few more titles out the gate than Android Wear managed. That’s academic for the moment, though, since you can’t actually go out and buy an Apple Watch yet.

Things get murkier on the fitness wearable side. Compared to mainstream models like Jawbone’s UP24 the Microsoft Band is a lot chunkier, though you do get that integrated display rather than having to check the app all the time.

Battery life is shorter as a result – Jawbone’s latest firmware pushed the UP24 to as much as fourteen days use on a charge – and, unlike designs from Nike and others, you’ll still need a proprietary charging cable rather than regular USB.

Microsoft also has an edge in the actual range of fitness activities its wearable supports. Whereas most of the trackers do just that – keep an eye on your performance – they’re reliant on the wearer themselves figuring out what kind of exercise to attempt.

Wearable Technology Developer Exclaims Massive Adoption Potential

Wearable technology developer exclaims massive adoption potential

Those of you unfamiliar with Powell’s work, you can hit up the following three links and see the videos of the projects he’s done throughout this post. Some of the products Powell uses are the Vuzix STAR 1200 AR glasses, Raspberry Pi – the fabulous miniature computer, and of course, a good ol’ fashioned ASUS Eee Pad Transformer.

• Raspberry Pi takes on Google’s Project Glass

• DIY Project Glass makes Google’s AR vision real

• Will Powell brings on AR vision real-time translation

SlashGear: Where you working with wearable technology before Google’s Project

Glass was revealed to the world?

Powell: Yes at Keytree we were working with wearable technology before the unveiling of project glass. I was working on CEO Vision a glasses based augmented reality that you could reach out and touch objects to interact or add interactive objects on top of an iPad. I have also had lots of personal projects.

SG: What is your ultimate goal in creating this set of projects with

Raspberry Pi, Vuzix 1200 Star, etc?

P: I would say that the ultimate goal is really to show what is possible. With CEO Vision at Keytree we showed that you could use a sheet of paper to interact with sales figures and masses of data using the SAP Hana database technology. Then creating my own version of project glass and now extending those ideas to cover translations as well, was just to show what is possible using off-the-shelf technology. The translation idea was to take down barriers between people.

SG: Do you believe wearable technology will replace our most common mobile tech – smartphones, laptops – in the near future?

P: Yes I do, but with an horizon of a couple of years. I think that with the desire for more content and easier simpler devices, using what we are looking at and hearing to tell our digital devices what we want to find and share is the way forward. Even now we have to get a tablet, phone or laptop out to look something up. Glasses would completely change this because they are potentially always on and are now adding full time to at least one of our fundamental senses. Also many of us already wear glasses, according to Vision Council of America, approximately 75% of U.S. adults use some sort of vision correction. About 64% of them wear eyeglasses so people are already wearing something that could be made smart. That is a huge number of potential adopters for mobile personal information delivery.

SG: What projects do you have coming up next?

P: I have many more ideas about what glasses based applications can be used for and am building some of them. I am creating another video around translation to show the multi lingual nature of the concept. Further to that, we are looking at what areas of everyday life could be helped with glasses based tech and the collaboration between glasses users. The translation application highlighted that glasses are even better with wide adoption because Elizabeth could not see the subtitles of what I was saying without using the TV or tablet.

Stick around as Powell’s mind continues to expand on the possibilities in augmented reality, wearable technology, and more!

Fun With Iphone: How To Swap Two Faces In A Photo With Faceplant

Sometimes there’s no greater humor than that of which you can dish out by way of photo editing.

If you’ve ever wanted to swap two faces in a photograph for the giggles you know it’s going to bring to the table between yourself and other people, then you’ve come to the right place.

In this tutorial, we’ll show you how you can use a simple and free iPhone app to quickly swap two faces in a photograph right from your device.

Swapping two faces in a photograph from your iPhone

This tutorial will feature the free FacePLANT app from the App Store, which lets you swap faces in photographs you take with the camera on your device, or from images you already have saved in your Camera Roll.

You can either take a fresh photograph or you can pick one from your Camera Roll, and follow these steps:

1) Download and install FacePLANT from the App Store for free.

2) Open the app and tap on either the Choose Photo or Take Photo button. You’ll pick the choose option if you have a photo in your Camera Roll already, or the take option if you intend to take a fresh photo.

3) You will have to grant the app access to your Camera Roll or to your Camera, depending on what you chose. When you do, choose or take a photo. The app will then automatically swap two of the most distinguishable faces.

4) Once swapped, you can tap on the face to select it and then you pinch to zoom or rotate, double-tap to flip the face 180º horizontally, and you can even modify the face’s brightness, contrast, and saturation levels to match the rest of the head to your liking.

5) When you’re satisfied with your settings, deselect the face you’re working on by tapping anywhere in the app’s blank space, and then tap on the Next button.

6) When you’re done, you can tap on the Share button to reveal a list of sharing options. We recommend saving the image to your Camera Roll and then sharing it from an iOS share sheet.

And that’s all you have to do to swap two faces in a photograph on your iPhone. The finished product looks pretty legit if we do say so ourselves!

FacePLANT, a fun app

My general opinion of the FacePLANT app when it was originally referred to me was, “wow this is funny.” I still hold that opinion. I use this app constantly for pranks and laughter when I’m with friends or family and want to have a good time.

I particularly like how you can use existing photos from your Camera Roll, or take fresh ones. The only catch is you need to use a photograph where both faces are clearly visible and are looking right at you. Faces that are taken from the side don’t work too well.

There are many other face-swapping apps to pick from, but I’ve selected FacePLANT simply because I’ve used it so much and it just works. Some alternatives to look at include:

Conclusion

FacePLANT, which can be had for free from the App Store, is a fun way to swap two faces in a photograph. I find it’s great for entertainment, such as parties, hanging out with friends, or when you’re trying to share a good laugh with someone.

New Sony A9 Camera Revealed

New Sony A9 camera revealed [with sample photos]

This morning Sony showed off a new flagship 24-megapixel camera in the Sony A9, able to shoot photos very, very fast. This camera is able to shoot up to 20 frames per second with no blackout – that’s intense. It’s also able to capture at 1/32,000 of a second with vibration-free shooting using “Silent Shooting.” This is also the first full-frame stacked CMOS sensor with integral Memory “for 20x faster data readout speed” according to Sony.

The Sony A9 camera is able to fire off 241 Compressed RAW photos before hitting a buffer. This camera is also able to shoot 362 Jpeg photos before hitting a buffer. That’s also quite intense – right alongside AF/AE tracking up to 60 measurements per second.

The Sony A9 also goes by model number ILCE-9, if you’re wanting to look for it that way. This camera has a whole lot of specs to go over – the lot of them coming up to a camera that’a made for speed. The following bits make up a short need-to-know list of features on this machine.

Sony A9 Specs:

• World’s first full-frame stacked CMOS sensor, 24.2 MP resolution (35mm full frame (35.6×23.8mm), Exmor RS CMOS sensor)

• BIONZ X processing engine

• Battery Life: Approx. 480 shots (Viewfinder) / approx. 650 shots (LCD monitor) (CIPA standard)

• 95.6mm tall, 126.9mm long, 63mm deep

• Blackout-Free Continuous Shooting

• Up to 20fps for up to 241 RAW/ 362 JPEG images

• Silent, Vibration-free shooting at speeds up to 1/32,000 sec

• 693 point focal plane phase detection AF points with 60 AF/AE tracking calculations per second

• Ethernet port for file transfer

• Dual SD card slots

• 5-axis in-body image stabilization

The photos you see below are Sony A9 sample photos – proper photos captured by that device by professional photographers.. They are made a bit smaller – compressed by Photoshop – before uploading to the internet. To see full-sized shots, head to Sony and their own gallery with a few more details. You’ll need a lens like the Vario-Tessar T FE 16-35mm F4 ZA OSS or the FE 70-200mm F2.8 GM OSS to accomplish this sort of set of shots.

Above you’ll see a few features and/or accessories that can work with the Sony A9. The double-battery image shows two batteries in an attached optional vertical grip. The image with the circular arrows demonstrates (1) Yaw (2) Pitch (3) Roll in the total of 5-axis stabilization. The other white x-ray image shows the Quad-VGA OLED Tru-Finder. Below you’ll see an example of 4K video captured by the A9 and uploaded to YouTube (so there’s some compression, but still – that’s the same venue most video goes to anyway.)

This camera is priced such that the Sony A7II will be the least expensive in the Sony fullframe mirrorless collection. The Sony A9 will cost users a cool $4,499 USD, right out the box. This camera will become available for pre-order on the 21st of April, 2023.

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