How would our lives be different without music? Or without a chance to hear the voices of our loved ones? The truth is, we rarely think of how our brain is processing different sounds on a daily basis and how important it is to us.
Well, that’s no less significant for modern technology. Today, artificial intelligence is a powerful tool for analyzing data and recognizing various patterns hidden in that data. While image or video recognition is more or less clear, AI sound recognition technology is a tricky one.
Audio data contains more complex and intricate patterns for machines to analyze and process, thus making it a high-level task for machine learning researchers. How do they tackle the challenge using deep learning, and why do they need it? In this article, we’ll provide the answers to each of these questions.
What Is Audio Data Processing in AI?
For humans, audio data is any type of sound, music, or speech that we can easily recognize and understand the meaning behind. For machines, however, audio is unstructured data that needs to be thoroughly analyzed and prepared for further processing by an ML or DL model. Audio data delivers a wealth of helpful information and valuable insights if it is organized into a comprehensible format for machines.
In AI sound recognition, there are several terms you need to get familiar with. They are audio, sampling, sample, sampling rate, amplitude, frequency, as well as time domain and frequency domain. Also, there are different audio formats to deal with, such as WAV, MP3, and WMA. To spare the details of complex technical concepts, we’ll focus more on the audio analysis itself and explain it to you in the simplest terms possible.
Transforming, analyzing, and interpreting audio data captured by electronic gadgets is the process known as audio analysis. It makes use of a variety of modern technologies, including cutting-edge deep learning algorithms. The ultimate goal is to train machines to comprehend the different sounds we produce. Audio analysis has already been widely recognized across a range of sectors, including industry, healthcare, as well as media and entertainment.
Audio Analysis Applications for Machine Learning
In machine learning, audio analysis is a complex process of gaining useful knowledge from audio data by turning it first into a machine-readable format, aka labeling it. ML techniques for this task include digital signal processing, mel-frequency cepstral coefficients, and filter banks. They help classify, analyze, and describe audio data, and this capacity that machines developed has already found applications in many areas of our lives.
The most common usage scenarios of audio analysis include:
Instead of separating individual words, voice recognition can recognize people by the distinctive features of their voices. This type of audio analysis is used in security systems for user authentication (the banking sector, for example).
Speech recognition is computers’ capacity to recognize spoken words using NLP (natural language processing). Instead of manually typing text, we may use voice commands to operate our digital gadgets. Popular speech recognition technologies include Siri, Alexa, Google Assistant, and Cortana.
This method of audio analysis in machine learning is a widely used feature in popular apps (e.g., Shazam). It allows users to identify unfamiliar songs from a brief sample. Genre categorization is another area of application of music recognition technology.
Environmental sound recognition
In this case, the audio analysis focuses on identifying the sounds we hear around us. It is essential for IoT applications to comprehend the environment. For instance, it enables the car to modify for better safety of the driver. Monitoring the condition of equipment to avoid expensive failures is another illustration of this type of audio analysis. It also enables a non-intrusive method for remote patient monitoring in healthcare.
The Process of Audio Analysis Using Deep Learning
The majority of audio apps based on deep learning interpret audio via spectrograms. They often adhere to the same process, which starts with a wave file containing only unprocessed audio recording. The subsequent process involves creating a spectrogram from the audio. Using basic audio processing methods, one can improve the data contained in the spectrogram. Besides, one can enhance or clean the original audio file before it will be converted to a spectrogram.
As the image data is ready to work with, it can be analyzed and processed using CNN (convolutional neural network) architectures. This step is crucial for extracting feature maps. Depending on the issue that has to be resolved, the following stage is to produce output forecasts using this encoded form.
Every case is unique, so you’d be using different deep learning models for each specific task in audio analysis. Case in point, a classifier is used for the purpose of audio categorization. However, if you’re dealing with a speech translation into text, you need RNN layers. They help retrieve sentences from the encoded representation.
Keep in mind that a lot of technical details are left out to give you a general idea of how deep learning is used for audio recognition tasks in machine learning.
Top 6 Audio Analysis Tasks that Deep Learning Solves
There are countless types of audio data that may be encountered in daily life, including music compositions, spoken language, animal sounds, various natural noises, and sounds produced by human activities.
Thus, it is more than expected that many scenarios call for us to structure and evaluate audio data, given the presence and variety of it in our life. Since deep learning methods have progressed, it may now be used to address a variety of audio analysis tasks.
1. Audio Classification
The process of classifying a sound by choosing from a number of categories. However, identifying the source of a particular sound may be challenging. Deep learning makes this a valuable solution for surveillance systems (identifying security break-ins or a piece of equipment that is failing based on the sound it makes).
2. Voice Recognition
This classification issue deals with recognizing spoken sounds. It can specify the name or gender of the person speaking. It’s possible to infer someone’s mood from the tone of their speech and recognize a specific human emotion. This might also be used to distinguish the kind of animal making a sound, as well as to determine its emotional undertone.
3. Audio Separation & Segmentation
In audio separation, a concerning audio signal is separated from different sounds for the following analysis. If there are sounds in the background, you can pick out certain sounds from it. The important portions of the audio stream are highlighted using audio segmentation and can be used for diagnostic reasons.
4. Speech to Text & Text to Speech
To elevate audio analysis capabilities, deep learning can be used to comprehend what the person is saying. To do this, the words from an audio file must be extracted in the corresponding language and converted into text. To distinguish unique words from spoken sounds, the system must learn some fundamental language skills in addition to dealing with audio analysis and NLP. With speech synthesis, one may go the other way and take written material and turn it into speech.
5. Classification & Tagging of Music Genres
The ability to recognize and classify music by simply analyzing audio data has become more and more popular with the rise of music itself. Such a deep learning solution can determine what genre the song belongs to by examining its content. It can also be helpful in making music suggestions based on users’ music preferences.
6. Music Generation & Music Transcription
In the real world, deep learning has a wide range of sophisticated applications. We can now create synthetic audio, which is appropriate for a certain genre, piece of music, or perhaps even style. Music transcription sort of uses this skill backward. To generate a music notation sheet using the notes from the song, you need some annotated acoustics. Here at https://labelyourdata.com/services, a team of data pros can lend a hand on completing this task for you.
By using even the smallest piece of audio data, deep learning algorithms can provide us with a lot of useful information. This can be anything from identifying a person speaking, tone of voice, human emotion, or even deciphering the meaning of audio context.
Since most audio analysis systems employ spectrograms to interpret audio, deep learning helps elegantly capture the key elements of audio data as an image or text. As a result, we have numerous game-changing applications of audio recognition technology already transforming our lives and major industries. What comes next?