Revolutionizing Audio Processing Though Deep Learning

The introduction of transformative technologies such as neural networks and deep learning has revolutionized the audio industry by changing the way it processes audio data. Initially, the processing was dependent on traditional signal processing systems such as Gaussian mixture models, hidden Markov models, etc., requiring industry-specific experts to refine and mould the audio data for use. However, recently the industry has embraced techniques from image processing technologies found in deep learning to convert raw audio data into images to process the information. Architectures such as convolutional neural networks (CNN) and long short-term memory (LSTMs) have played an integral role in eliminating advanced preparation to deal with signal processing, enabling simplification of the process.

Components and Audio Features of Audio Data Processing

In audio data processing, the components of data are namely, time, frequency (denoted as Hertz or Hz), and amplitude (denoted as Decibels or dB). A signal is created by amalgamating time and frequency in a chronological method. 

a) Time and Frequency Domains

Signals with varying strength, also known as amplitude at different points in time, when visualized as a picture are known as a Waveform. It is a part of the time domain and represents time against amplitude but misses the aspect of frequency.  

Whereas, a spectrum is a collection of frequencies plotted according to their amplitude and represents any sound that we hear. It is a part of the frequency domain and showcases frequency against amplitude but misses the time aspect. 

Fourier Transform (FT) helps to transform waveforms into spectrums by decomposing the signal into a multitude of frequencies and amplitudes. It utilizes algorithms such as Fast Fourier Transform (FFT) and Short Fast Fourier Transform (SFFT) to compute signal processing for audio analysis and further applications.

b) Audio Features

Spectrogram helps to visualize all three components of audio by visualization of signals with different frequencies arranged over time and amplitude. Time is represented on the x-axis, frequency is represented on the y-axis and amplitude is represented by the intensity of color of the signals. 

Mel Spectogram is based on the Mel scale, a scale that converts frequencies in values of Hertz (Hz). It is used for processing sound signals that can be heard by the human ear and is crucial in understanding how humans perceive sound characteristics. The human ear is better equipped at hearing lower frequencies than higher frequencies and the mel scale is especially useful for covering this human aspect in audio analysis.

MFCCs or mel frequency cepstral coefficients up till recently played a frontal role in audio data processing. It encaptures and describes the image of the spectral envelope in a sound signal and enables compressed representation for classification of audio data. Its procedure involves splitting up audio signals in frames, and applying dFT (discreet Fourier Transform) and amplitude spectrum. Then it is followed by the application of Mel scaling and smoothing and finally takes discreet cosine transform to develop MFCCs features.  It has wide usage in speech processing.

Zero Crossing Rate is one of the important acoustic features. It is especially useful in understanding differences between highly correlated and uncorrelated features of audio data and measures how signals go from positive to negative or zero and vice versa. It measures the smoothness of a signal within the segment and how many times the signal crosses the horizontal axis in the frame. It helps in detecting and separating music from background noise and the absence of sound from the frame.

Also Read: Integrating AI and Machine Learning in HRM Software Development

Deep Learning Models Used in Audio Data Processing

1. Convolutional Neural Network (CNN): It is a deep learning neural network architecture consisting of features like convolutional layers, pooling layers and deeply connected layers. It is famous for its efficiency in image classification. To attain a similar level of accuracy in audio analysis, it treats spectrograms like images and uses robust architectures and large data sets for classification of audio data. Spectrograms have displayed best results in the procedure of audio analysis among other audio features like MFCCs, raw audio, etc. 

2. Long Short-Term Memory Models (LSTMs): LSTMs are a type of architecture in Recurrent Neural Networks (RNN) in deep learning which are designed to model sequential data and are useful for predicting patterns. It is able to detect learning dependencies and memorize information from previous steps. It has a wide range of applications including speech-to-text and speech recognition. Based on memory, its state is updated every time fresh data enters the network. Its use has been found in both the time domain and frequency domain and has proved to be more accurate and adaptable than its counterparts. 

Applications in Audio Processing

1. Audio Classification: Audio classification is concerned with identification of audio data and tagging into different categories they are related to. Convolution neural network (CNN) is the most popular tool in classification of audio due to its proven efficiency in image classification. The audio is first pre-processed to identify any familiar features. Once the existence of similar features are identified, they are grouped together in classes for further application. 

2. Audio Segmentation: Once the features of the audio are identified, it is segmented and isolated from a mixture of background noises like echo and overmodulation. If the audio supposedly does not possess the required characteristics, audio segmentation helps to segment the audio data into portions having desired features such as length, similarity and highlight relevant portions for the next stage of processing.

3. Speech: Audio processing in speech recognition has seen immense potential with neural networks in latest research developments. CNN and LSTM have shown great results in proving accuracy rates in identifying features of speech which can be further used for a wide range of applications. CNN allows to extract meaningful words and formulation texts on the basis of that speech. It helps to solve the problem of multi-classification tasks and has great usage in the IoT industry among many others.

NLP for speech recognition: NLP helps to analyze human speech by converting them into text by utilizing deep-learning neural networks, machine learning, and various technologies to understand the context. By understanding the context of what is being said, NLP makes it possible for computers to respond to commands, and carry out relevant tasks to achieve pre-determined objectives. Multiple examples of NLP can be easily found in the form of customer service bots, adaptive email filters, AI device assistants, GPS systems, diction softwares, etc.

4. Music: In 2022, the revenue of music industry worldwide was found to be worth five billion US dollars. An industry of such immense scale requires high-quality audio and fast output to compete and hence, requires fast audio processing technologies to match its requirements. With several applications such as music retrieval, tagging, recommendation, onset detection, and beat tracking, audio processing is a heavily used technique in the industry. Deep learning neural networks are able to handle these situations with ease as compared to traditional algorithms employed in the past. Music Recommendation utilizes algorithms to suggest listeners new songs on the basis of their past listening activity and Music Tagging helps to formulate new metadata for the audio so it can be retrieved later.

5. Environmental Sounds: Beyond the usage of speech recognition and music generation, systemic analysis for environmental sounds has had great requirements for audio processing to derive useful information from raw audio data. Some of the applications for environmental sounds are surveillance, acoustic event detection and classification, media tagging etc. Acoustic scene classification aims to label an entire recording to one scene label, whereas acoustic event detection is concerned with estimating the beginning and ending times of individual scene and categorizing them according to one label, and tagging helps to estimate numerous sound classes without having access to temporal information.

Benefits of Deep Learning in Audio Data Processing

1. Ease of Use with Unstructured Data: Deep learning neural networks work well with unstructured data which is especially prevalent in the audio industry. Human voice in general does not have a systemic quality to it which makes it difficult for many systems to identify and process audio data. However, with the help of NLP technologies utilizing CNN and LSTMs, they are able to break down speech into simpler pieces of input and analyze them for their meaning and future requirements. These technologies are especially helpful in B2C industries which makes understanding customer behavior more accessible in the way they interact with systems for completing their requirements. This information can then be processed to understand patterns, make informed decisions and implement necessary improvements for better results.

2. Simpler Extraction of Audio Features: To utilize raw audio data, systems need to extract useful features in order to process it for further use. Deep learning neural networks with the help of spectrograms, MFCCs, etc., enable processing systems to extract useful elements from raw information, eliminate unwanted data and convert them into meaningful digital inputs for their intended use such as classification, music generation, etc.

3. Enhanced Audio Quality: With the ability to isolate elements of audio, deep learning neural networks allow to enhance the quality of raw audio through removal of any unwanted background noises. In music generation, it facilitates refinement by filter application for improved quality on the basis of artist recognition, beat tracking, genre classification, analysis of rhythm, melody, mood, etc. which improves the overall production quality of audio and brings it closer to its intended purpose. 

4. Reduction in Cost and Time: Before deep learning neural networks, the audio industry had to bring in industry-specific experts and use technologies that were expensive and time-consuming to use and had comparatively low accuracy rates. Technologies such as Machine learning, Artificial Intelligence, and architectures like CNNs and LSTMs from deep learning allow audio industries to have access to large audio databases and algorithms that continuously keep reinventing themselves as they interact with new data. With increased accessibility to powerful processing applications and huge databases, industries are able to save time and expenses as processing audio data becomes comparatively easier. 

Future of Audio Processing

With the introduction of deep learning neural networks, audio processing has now access to great computational abilities and large databases. This has led to ease of use in refining audio data along with higher accuracy in detecting and eliminating unwanted noises. Along with greater efficiency, audio processing is now able to cut down time for the complete procedure by shortening the duration of classification, segmentation and tagging tasks. With even greater advancements in technology, the use of audio processing is guaranteed to increase as it has proved its potential in interacting and analyzing with both systemic and human counterparts. Businesses require expert-led solutions and robust technologies to improve operational efficiency and to lead the competition for optimum performance. If you are looking for similar cutting-edge solutions for your company, we’d love to support your journey. Feel free to leave a query and our experts will contact you within 24 hours.