How can Machine Learning be used in Audio Analysis? | by Suhas Maddali | Jan, 2023
Learn how machine learning could be used to analyze audio signals and generate predictions for both classification and regression tasks
Machine learning has been gaining rapid traction in the recent decade. In fact, it is being used in numerous industries such as healthcare, agriculture, and manufacturing. There are a lot of potential applications of machine learning being created with the advancement of technology and computational power. Since data is available in various formats in abundance, it is the right time to use machine learning and data science to extract various insights from data and make predictions using them.
One of the most interesting applications of machine learning is in audio analysis and understanding the quality of different audio formats respectively. Therefore, using various machine learning and deep learning algorithms ensures that predictions are created and understood with the audio data.
Before doing audio analysis, samples of the signals must be taken and analyzed individually. The rate at which we would be taking the samples is also known as the sampling rate or Nyquist rate. It would be really handy to convert a time-domain signal to a frequency domain to get a good logical understanding of the signal along with computing useful components such as power and energy. All these could be given as features to our machine learning models which would use them for making predictions.
There is a popular conversion of an audio signal to a spectrogram (image) so that it could be given to convolutional neural networks (CNNs) for prediction. A spectrogram can capture important characteristics of an audio signal and give representations in 2D which can hence be used with image-based networks.
There are plenty of ML models that perform a very good task of predicting the output labels if they are given an image. Therefore, an audio signal which is composed of amplitude along with different units of frequency, could also be converted to an image and used for robust ML predictions.
In this article, we would be going over how to read an audio file by considering a random example and plotting it to understand its graphical representation. Later, we will perform feature engineering with the image data and perform convolutional operations as the audio is converted to an image. Finally, we will get sample predictions for unseen data. Note that this code is used for demonstration and does not take into account specific datasets.
Reading the data
We are going to import necessary libraries that are used for reading an audio file in the form that is mostly present in the ‘.wav’ format. After reading the file, we are getting an array representation as shown in the code cell above. Finally, we would be plotting the output just to see how it looks with matplotlib.
Feature Engineering
Now that the data is plotted and visualized to see anomalies in the ‘.wav’ file, we would now be using a popular library called ‘librosa’ that can be used to calculate the short-term Fourier transform of the audio data. This is to ensure that the signal is decomposed into its constituent frequencies and is a technique that is widely used in a large number of industries.
Training the Models
Now that we have used ‘librosa’ to get the frequency components, we would be using machine learning models to make predictions. It is to be noted that it is a classification problem and hence, we go ahead with using a random forest classifier. However, feel free to use any other machine learning model that suits your needs and the business.
We are now going to be using the same code but for regression tasks where the output is continuous instead of discrete. Below is the coding cell about how training could be done and performance monitored with the help of a random forest regressor.
Hyperparameter Tuning
It is important to determine the right hyperparameters for the model (Random Forest) before it could be deployed in real-time. There are a lot of hyperparameters to search for when it comes to deep neural networks. Since we are using random forests as our baseline models, we should be able to get the right hyperparameters in a minimal search space. Let us see how to perform hyperparameter tuning on the general dataset.
In the code cell, we specify the number of estimators and the maximum depth of the tree that we search for to get the best results on the test set. We finally monitor the score and see how changes in the hyperparameters can lead to better performance by the model.
Model Deployment
Now that we have performed hyperparameter tuning to give the most accurate predictions, it is time to save the machine learning models that provided the best results. Therefore, we would be using the pickle library in python so that we would be having the freedom to save the machine learning model that could be later used for serving.
After saving the model, we would again load it when we are building a production-ready code and use it to make predictions on incoming batches or streams of data. It is to be noted that the set of steps that were used to perform featurization during the training data must also be performed on the test set so that there is no skew in the data.
Constant Monitoring
We know that the model is performing a good job in serving data where it is receiving incoming data from users, it is also an important yet neglected step which is to monitor the predictive quality of the models. There are often scenarios where the models might not be performing as they were during the training. This can be because there is a difference between the training and serving data. For example, there can be situations such as concept drift or data drift that can have a significant impact on the performance of the inference model that is put into production.
Constant monitoring ensures that steps are taken to check the predictive models and understand their behavior with changing data. If the predictions are the least accurate and it is leading to a loss of revenue for the business, steps should be taken to again train the models with this deviant data so that there is no unexpected change in the behavior of the models respectively.
Conclusion
After going through this article, you might have gotten a good idea about how to perform machine learning for audio data and understand the overall workflow. We have seen steps such as reading the data, feature engineering, training the models, hyperparameter tuning, model deployment along with constant monitoring. Applying each of these steps and ensuring that there are no mistakes when developing a pipeline would lead to a robust machine learning production system.
Learn how machine learning could be used to analyze audio signals and generate predictions for both classification and regression tasks
Machine learning has been gaining rapid traction in the recent decade. In fact, it is being used in numerous industries such as healthcare, agriculture, and manufacturing. There are a lot of potential applications of machine learning being created with the advancement of technology and computational power. Since data is available in various formats in abundance, it is the right time to use machine learning and data science to extract various insights from data and make predictions using them.
One of the most interesting applications of machine learning is in audio analysis and understanding the quality of different audio formats respectively. Therefore, using various machine learning and deep learning algorithms ensures that predictions are created and understood with the audio data.
Before doing audio analysis, samples of the signals must be taken and analyzed individually. The rate at which we would be taking the samples is also known as the sampling rate or Nyquist rate. It would be really handy to convert a time-domain signal to a frequency domain to get a good logical understanding of the signal along with computing useful components such as power and energy. All these could be given as features to our machine learning models which would use them for making predictions.
There is a popular conversion of an audio signal to a spectrogram (image) so that it could be given to convolutional neural networks (CNNs) for prediction. A spectrogram can capture important characteristics of an audio signal and give representations in 2D which can hence be used with image-based networks.
There are plenty of ML models that perform a very good task of predicting the output labels if they are given an image. Therefore, an audio signal which is composed of amplitude along with different units of frequency, could also be converted to an image and used for robust ML predictions.
In this article, we would be going over how to read an audio file by considering a random example and plotting it to understand its graphical representation. Later, we will perform feature engineering with the image data and perform convolutional operations as the audio is converted to an image. Finally, we will get sample predictions for unseen data. Note that this code is used for demonstration and does not take into account specific datasets.
Reading the data
We are going to import necessary libraries that are used for reading an audio file in the form that is mostly present in the ‘.wav’ format. After reading the file, we are getting an array representation as shown in the code cell above. Finally, we would be plotting the output just to see how it looks with matplotlib.
Feature Engineering
Now that the data is plotted and visualized to see anomalies in the ‘.wav’ file, we would now be using a popular library called ‘librosa’ that can be used to calculate the short-term Fourier transform of the audio data. This is to ensure that the signal is decomposed into its constituent frequencies and is a technique that is widely used in a large number of industries.
Training the Models
Now that we have used ‘librosa’ to get the frequency components, we would be using machine learning models to make predictions. It is to be noted that it is a classification problem and hence, we go ahead with using a random forest classifier. However, feel free to use any other machine learning model that suits your needs and the business.
We are now going to be using the same code but for regression tasks where the output is continuous instead of discrete. Below is the coding cell about how training could be done and performance monitored with the help of a random forest regressor.
Hyperparameter Tuning
It is important to determine the right hyperparameters for the model (Random Forest) before it could be deployed in real-time. There are a lot of hyperparameters to search for when it comes to deep neural networks. Since we are using random forests as our baseline models, we should be able to get the right hyperparameters in a minimal search space. Let us see how to perform hyperparameter tuning on the general dataset.
In the code cell, we specify the number of estimators and the maximum depth of the tree that we search for to get the best results on the test set. We finally monitor the score and see how changes in the hyperparameters can lead to better performance by the model.
Model Deployment
Now that we have performed hyperparameter tuning to give the most accurate predictions, it is time to save the machine learning models that provided the best results. Therefore, we would be using the pickle library in python so that we would be having the freedom to save the machine learning model that could be later used for serving.
After saving the model, we would again load it when we are building a production-ready code and use it to make predictions on incoming batches or streams of data. It is to be noted that the set of steps that were used to perform featurization during the training data must also be performed on the test set so that there is no skew in the data.
Constant Monitoring
We know that the model is performing a good job in serving data where it is receiving incoming data from users, it is also an important yet neglected step which is to monitor the predictive quality of the models. There are often scenarios where the models might not be performing as they were during the training. This can be because there is a difference between the training and serving data. For example, there can be situations such as concept drift or data drift that can have a significant impact on the performance of the inference model that is put into production.
Constant monitoring ensures that steps are taken to check the predictive models and understand their behavior with changing data. If the predictions are the least accurate and it is leading to a loss of revenue for the business, steps should be taken to again train the models with this deviant data so that there is no unexpected change in the behavior of the models respectively.
Conclusion
After going through this article, you might have gotten a good idea about how to perform machine learning for audio data and understand the overall workflow. We have seen steps such as reading the data, feature engineering, training the models, hyperparameter tuning, model deployment along with constant monitoring. Applying each of these steps and ensuring that there are no mistakes when developing a pipeline would lead to a robust machine learning production system.
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