ML Deep Learning for Audio Analysis
Despite the fact that audio analytics and signal processing have tremendously profited from machine learning and deep learning approaches, the wider data science education field largely neglects these and NLP and machine imaging are more prevalent. Transforming, investigating, and analyzing audio information captured by digital equipment is the process of audio analysis.
In this article, we’ll study machine learning and deep learning applications connected to audio in an effort to somewhat equalize the issue.
Sound data:
Sound data is different from most other forms. To start with, a variety of wave frequencies and intensities combine to form a single instance of sound data. We convert data into a tabular or matrix-like data collection before performing any machine learning.
A second aspect to consider is time. Given that sound fragments are of a certain duration rather than a capture at a single moment in time, it is really more analogous to video data than to picture data.
Data preparation:
Audio data contains a lot of variations. Snippets can have a varied range of channels or be sampled at a varying rate. The lengths of the clips will likely vary. It is necessary to carry out some data cleaning procedures to standardize the dimensions of our audio data because deep learning models necessitate that all input items will be of a similar size and dimensions. We resample the audio to ensure that each component has the same sampling rate.
Additionally, we transform each item to the same audio length. We accomplish this by padding the shorter sequences or truncating the larger sequences. If the data is of subpar quality, enhancing may need to be carried out.
Feature extraction:
Next, we extract the characteristics that are required to train a model. To do this, it is possible to employ the same methods used to accurately categorize photos to produce a visual representation of each of the audio samples that will enable us to identify characteristics for categorization.
We can see the frequency spectrum of a sound and how it changes in a very little amount of time using spectrograms. It is possible to transform raw data into a spectrogram, which is a matrix of frequencies in audio, obtained by decomposition.
Audio Analysis Algorithms:
1. Baidu’s Deep Speech model:
This model is essentially a regular CNN + RNN that also has a loss algorithm involved. Any standard convolutional network has a few residual layers, which process input spectrogram pictures and produce feature maps of those images. A typical recurrent network has a few Bidirectional LSTM layers that process the feature maps as a series of separate timesteps or “frames” that line up with the required outputs.
Essentially, the given algorithm transforms feature maps, which describe the audio in a continuous manner, into discrete representations. Between the convolution and recurrent networks, there are additional linear layers that assist in reshaping the inputs of one network into the outputs of the other.
Our algorithm then provides character probabilities for each timestep, or “frame,” in the Spectrogram pictures.
2. CTC Algorithm:
This algorithm is called Connectionist Temporal Classification. CTC is applied to match the inputs and outputs once the input and output series are discrete and continuous, respectively, and there are no distinct component limits which could be employed to translate the data to the final sequence’s components.
This is unique since it dynamically accomplishes this alignment; you do not need to explicitly include that orientation as part of the labeled training data. The cost of producing the training datasets would have been prohibitive as a result.
3. WaveNet:
Google DeepMind created WaveNet, a generative network for unprocessed sound that is centered on deep learning.
WaveNet’s primary goal is to create fresh examples from the data’s initial dispersion. Consequently, it has the name “Generative Model.” An NLP language model attempts to anticipate the following word given a list of words. In WaveNet, given a series of samples, it attempts to predict the following sample, much like a language model.
4. Next Generation End-to-End:
In contrast to the 2-Stage procedure, which predicts the whole waveform, a DNN model can only predict the sequence of waveform blocks (1-D target signal divided into no overlapping segments).
The block-auto regressive waveform creation increases training speed. As opposed to classic neural vocoders like WaveRNN, each step creates a new block in parallel.
Remove the requirement for a Neural Vocoder by directly predicting sequences without producing any intermediate representations or Mel-spectrograms.
As a result, the model design and training necessary to achieve quick waveform generation are greatly simplified.
Applications of audio analysis:
The use of audio analysis has already been widely accepted across a range of sectors, including industry, healthcare, and entertainment. We’ll list the most common usage scenarios below.
1. Speech recognition:
Speech recognition refers to the capacity of computers to recognise spoken words using methods of natural language processing. We may use voice commands to operate PCs, cellphones, and other gadgets, and we can dictate messages to computers rather than manually typing them. Popular instances of how technology has permeated our daily lives are Siri by Apple, Alexa by Amazon, Google Assistant, and Cortana by Microsoft.
2. Speech to text:
TTS uses machine learning techniques to simulate human speech on a computer from a written representation. Typically, developers employ speech synthesis to make voice robots like IVR (Interactive Voice Response)
3. Audio classification:
When using deep learning, among the most commonly used applications is audio classification, where the model is trained to categorize sound relying on audio attributes. It will forecast the labels for that audio characteristic provided the inputs. These are utilized in a variety of industrial applications, such as categorizing brief speaker utterances and music genres.
Python code for processing an audio signal:
The code first imports the necessary modules and then loads an audio file using the wavfile module. Next, it applies a low-pass filter to the audio data using the signal.butter and signal.filtfilt functions from the scipy module. It then applies a high-pass filter to the data in the same way. Finally, the filtered data becomes a new audio file using the wavfile module.
Low-pass filtering and high-pass filtering are two common techniques used to process audio signals.
Low-pass filtering involves removing frequencies above a certain cutoff frequency from the signal, allowing only the lower frequencies to pass through. This can be useful for removing high-frequency noise or for creating a smoother, more mellow sound.
High-pass filtering involves removing frequencies below a certain cutoff frequency from the signal, allowing only the higher frequencies to pass through. This can be useful for removing low-frequency noise or for emphasizing higher frequencies in the signal.
In the example code provided, we apply low-pass filtering to the audio data first, followed by high-pass filtering. This would result in the removal of both low-frequency and high-frequency noise, leaving only the frequencies in the middle range of the audio spectrum.
Both low-pass filtering and high-pass filtering can be implemented using digital filters, which use mathematical operations to process the signal. In the example code,we use the signal.butter function from the scipy module to design a Butterworth filter, which is a type of digital filter that is commonly used for audio signal processing. We also use the signal.filtfilt function to apply the filter to the signal.
Conclusion:
This article has delved deep into applying ML and DL principles in audio analysis. We have gone through the basics of sound from a physics point of view and also talked about how to handle and process audio data, as well as how to make algorithms that are employed in audio analysis.