A lot of AI-powered solutions are focused on text processing. Despite being one of the most popular formats, text is not the only way of getting and transmitting information. 

We don't always notice that, but sounds are everywhere around us! Our brains are continuously processing and analyzing them. Quite often, it happens even unconsciously. Acoustic signals can tell us a lot about the environment. What is even more important is that we can get even more insights if we start applying the newest technologies to their processing. That’s what we are going to talk about in this article - audio analysis with machine learning.

What is audio analysis?

This term can be defined as the process of transforming, examining, and interpreting audio signals. All these actions are aimed at extracting meaningful information. This process includes the application of different techniques and methods. Their exact set depends on the specific goals of the performed sound analysis.

Solutions with such functionality are actively gaining popularity in different domains. Among them, we can name gaming, entertainment, education, manufacturing, healthcare, and others. And yet, advancements in this sphere are moving forward! This means that quite soon, we are likely to observe the introduction of new cutting-edge solutions that may find their use in absolutely new spheres.

Applications of audio analysis with machine learning

Talking about the analysis of sounds, it’s worth mentioning its various use cases and applications. Below you can find a range of examples that are already gaining their adoption today.

Speech recognition

It is the ability of devices to identify and transcribe spoken words from an audio signal. Automatic speech recognition (ASR) is widely used in voice-to-text apps, automated customer services, and virtual assistants. Thanks to this type of AI sound recognition, we can enjoy hand-free experiences and control our devices with the help of voice commands. In other words, all these cases with “Hey Google, please turn off the light in the bathroom” are not magic but good examples of speech recognition applications.

Music analysis

You’ve heard about Shazam, right? And that’s exactly what we’d like to mention in this section. Shazam-like apps can analyze such elements of sounds as melody, harmony, rhythm, and tempo. As a result, they can be used for song recognition, music classification, and genre detection. Moreover, such audio detection tools can be applied to music recommendation systems.

Speaker identification (voice recognition)

Technologies can identify not only what is being said but also who is talking. It means that they can verify the identities of speakers based on their voice characteristics. This can be utilized for user authentication for security purposes, as well as for user experience personalization.

Environmental sound recognition

Such solutions are focused on the identification of noises around us and classifying specific sounds. They can be helpful in various domains. For example, in healthcare, they can help to analyze the sounds in the patient’s room. The sounds of falling or coughing may indicate the necessity for a nurse to visit a patient and provide assistance.

In manufacturing, this technology can be used to analyze machine noises. This can be crucial for enhancing predictive maintenance.

Noise reduction

This example of an audio machine learning analysis application is already widely known to users. It presupposes the identification and removal of unwanted noise from an audio signal to improve clarity and quality. Today, such features are included in solutions for communication systems, hearing aids, and audio production.

Sentiment and emotion analysis

These technologies help to analyze the emotional tone or sentiment expressed in spoken language. They are often used in customer service to provide better responses in call centers based on the overall emotional coloring of the speech. 

In the entertainment industry, sentiment recognition tools make it possible to offer emotionally responsive video games and interactive user experiences.

AI sound recognition: Fundamentals of audio data

Implementing AI-powered audio recognition can be a very promising idea for your solutions. But to do it in the right way, you should have at least the most general understanding of sound data.

What is sound data? It can be explained as analog sounds in a digital form that keeps all the key properties of the original ones. There are three main characteristics that should be taken into account in the context of audio detection and analysis.

• Time period. It is probably the simplest characteristic to explain. It shows how long a particular sound lasts in seconds, minutes, or hours.
• Amplitude. This is the intensity of the sound. It corresponds to the loudness of the sound and is measured in decibels (dB).
• Frequency. This characteristic indicates the pitch of the sound and is measured in Hertz (Hz). It shows the number of sound vibrations per second. The hearing range of a human covers the frequencies from 20 Hz to 20 kHz. Low-frequency are perceived as bass, high-frequency sounds are treble.

Audio is included in the category of unstructured data. However, it is possible to define its format and choose one of them for storing sounds.

• WAVE (WAV, Waveform Audio File Format). It’s a raw audio format that will allow you to store it without compressing it. The format was developed by IBM and Microsoft.
• AIFF (Audio Interchange File Format) by Apple. This format also helps keep files without compression.
• MP3 (mpeg-1 audio layer 3) by the Fraunhofer Society in Germany. This format is probably the most well-known one. It started gaining popularity together with the adoption of portable devices for listening to music. MP3 compresses files. Nevertheless, you still can enjoy rather high sound quality.
• FLAC (Free Lossless Audio Codec) by Xiph.Org Foundation. This format ensures compression but the quality is not lost.

All these formats have their pros and cons. Still, when it comes to audio deep learning and machine learning solutions, often you can’t just take a file in one of the mentioned formats and feed it to the model. The chosen data should be transformed so that a machine can work with it.

Audio data transformation and processing: How this happens

The data transformation includes several steps. All this is aimed at extracting valuable features from the raw audio data and converting it into a form that can be used by ML algorithms.

It’s interesting to mention that AI audio analysis is based not on listening but on working with images. There are relevant software tools that help to automate these tasks. But to have a deeper understanding of the topic, it is still required to get some basic info on the possible approaches to analyzing sound.

For example, a basic visual representation of a signal is a waveform. It demonstrates the changes in amplitude over time. However, it doesn’t show frequencies.

Another graph that is used for analysis is a spectrum or spectral plot. Unlike a waveform, it demonstrates the frequency of the sound and its amplitude. But looking at it, you won’t be able to see the time component.

A spectrogram covers all the main characteristics of sound. Looking at it, it is possible to understand the changes in frequencies over time and see a period of the full sound. Moreover, it also helps to detect patterns and problem areas.

Speaking about various approaches to analyzing sound, it’s also worth paying attention to the Fourier transform (FT). It is a mathematical function that helps to break a sound signal into spikes of amplitudes and frequencies. It is useful for better understanding signals and addressing problem areas and errors in them.

Then we have a Mel spectrogram that demonstrates the perception of sound characteristics by a human given the frequency of the sound. The analysis based on this visual representation plays an important role in genre classification, instrument detection, and emotion recognition.

In a very simplified form, the process of ​​audio data transformation and processing can be described as follows:

1. Taking the required data that is stored in one of the standard formats;
2. Preparing files for ML processing with the help of available software tools;
3. Analyzing visual representations of sound data and extracting useful audio features; 
4. Training the selected ML model on the extracted audio features.

Audio analysis with machine learning: ML models

Let’s have a closer look at the types of models and networks that can be used for working with audio data.

• Convolutional neural networks (CNNs). They are trained on mel spectrograms of signals. CNNs demonstrate the best efficiency for tasks like audio classification, music genre recognition, and emotion detection.
• Recurrent neural networks (RNNs). These models are used for sequential data tasks, including speech recognition and audio generation.
• Hybrid CNN-RNN models. These audio machine learning models combine the power of CNNs and RNNs. It means that they can cope with feature extraction from spectrograms and handling temporal dependencies. They are applied for speech recognition, audio event detection, and video-audio synchronization.
• Autoencoders. They can learn to compress and reconstruct data. This makes them useful for removing noise from audio signals or for unsupervised feature extraction.
• Audio transformers. They are designed specifically for audio tasks. They rely on attention mechanisms to focus on important parts of the signal and capture long-range dependencies. These models are used for end-to-end speech recognition.
• k-Nearest Neighbors (k-NN). This ML technique is widely applied for classification and regression tasks. It is based on the assumption that similar data points are likely to possess similar values or labels. They are efficient for simple audio classification tasks, such as classifying different types of bird songs based on extracted audio features.
• Support vector machines (SVMs). This ML model is useful in audio classification tasks as well. It is often chosen to work with smaller datasets where deep learning models might overfit. For example, this model can be applied for detecting specific audio events like gunshots or sirens in urban soundscapes.

How to choose the right model for performing sound analysis?

To make the best choice, keep in mind a couple of factors.

1. Type of tasks. As you can see, different models demonstrate the best performance for different tasks. That’s why first of all, you need to make up your mind on the exact purposes of using ML.
2. Availability of data. It’s important to understand how much data you have and whether it is labeled or not. Some of the models perform well only with small datasets.
3. Computational resources. Some deep learning models require significant computational power for training. At the same time, simpler models are less resource-intensive.

Tools and libraries for AI audio recognition and analysis

There is a row of powerful tools and libraries available for use in AI analysis of audio data. They offer a wide range of functionalities, from basic audio processing and feature extraction to more advanced possibilities. Let us share a couple of examples with you.

• librosa. It is a Python library for audio and music analysis. It can be used for feature extraction, music information retrieval, and audio preprocessing.
• PyDub. This library supports a variety of audio formats and provides functions for tasks like cutting, concatenating, and applying effects to audio files.
• openSMILE. This toolkit for extracting features from audio files is widely used in speech and emotion recognition.
• Essentia. This is a library for audio analysis and audio-based music information retrieval. It contains numerous algorithms for feature extraction, classification, and segmentation.
• TensorFlow & Pytorch. These are deep learning frameworks. They support the creation of neural networks for audio recognition tasks. 
• Kaggle. It hosts a variety of publicly available datasets for audio analysis. Among the available options, you can get access to datasets for speech recognition, sound classification, and music information retrieval.
• UrbanSound8K. This is a dataset of 8,732 labeled sound excerpts from urban environments. They are categorized into 10 classes such as sirens, drilling, and street music.
• DeepSpeech. This is an open-source speech-to-text engine, which is based on a deep learning architecture trained on large datasets.
• Matplotlib. This library allows for creating static, animated, and interactive visualizations in Python. It can be used to visualize waveforms and spectrograms.
• Sonic Visualiser. This app is intended for viewing and analyzing the contents of audio files. It provides a range of visualization and annotation tools for in-depth analysis.

Steps for implementing audio analysis with machine learning

Planning to launch a solution with such functionality? You definitely should know the key steps of its implementation.

1. Setting goals. First of all, you should clearly determine what tasks you are solving and what objectives you have.
2. Collecting and preparing a dataset. You should find the required data. You can either use public datasets or collect your own.
3. Preparing audio files. Your files should be prepared for further processing. You may need to change the formats of data, remove silent parts, normalize the signals, etc.
4. Feature extraction. It’s necessary to extract features like frequency and amplitude from the raw audio signal. Here, you may use the visualization tools that will transform your sound into images based on its characteristics.
5. Model selection and training. It is required to find the most appropriate ML model. It should correspond to your goals and datasets. It is also necessary to train it for your tasks.
6. Model evaluation and optimization. Your model should be properly tested. Optimizing it may also be required before deployment. 
7. Deployment, monitoring, and maintenance. When everything is ready, the model can be deployed in the desired environment and integrated with your app. It is recommended to continuously monitor the model's performance in the production environment. This will help to detect any degradation over time. With time flow, you may need to add new feature extraction methods or improve existing ones.

You can read more about ML model deployment in one of our previously published blog posts.

Challenges in sound analysis

Performing audio analysis with ML can be a very promising idea for projects of different types. However, it’s vital to remember about possible pitfalls that you can face. The more you know about them, the better you can prepare to address them.

• Quality of records. Differences in microphone quality, recording environments, and equipment can lead to variations in audio quality. All this may negatively impact the reliability of the analysis.
• Background noise. Real-world audio often includes some background noise like sounds or wind. Sometimes it can obscure the primary sound signal and complicate the analysis.
• Lack of labeled data. High-quality, labeled datasets are very important for training ML models. Nevertheless, you may have not enough labeled data for specialized audio analysis tasks.
• Computational complexity. Complex ML models may consume a lot of computing and memory resources. As a result, it can be very challenging to ensure their real-time performance.
• Multi-modal integration. Some tasks, such as video analysis, may require integrating audio analysis with analysis of other formats (modalities), like video and text. This can become a real challenge as models need to learn from and correlate multiple data sources.

Final word

Audio analysis with machine learning can be as helpful as tricky, in case you have never had any relevant experience. That’s why it’s always better to have a professional team by your side. And at Tensorway, we are always ready to give you a helping hand.

With our expertise in working with AI and ML models, we will be able to cope with any task. Just tell us about your idea and we will find the right approach to its realization. Let’s bring the digital future closer together!