Machine Listening is the process where artificial intelligence is used to make sense of sounds. It can extract useful information and interpret various audio sources, enhancing many technology-driven operations.
Imagine you have a robot friend, and it can hear all the sounds you can hear. The robot listens to the sounds around it—music, people talking, the sound of the wind, the noise of the traffic—and tries to understand what those sounds mean. That’s just like machine listening in AI.
Machine Listening is an element of computational auditory scene analysis (CASA) within artificial intelligence. It endows systems with the ability to listen to auditory inputs, extracting meaning, interpreting, and acting on auditory data similarly to how humans do with their auditory senses.
The logic behind Machine Listening lies in signal processing and machine learning techniques. In the initial stages, the machine collects data via microphones or other audio sensors. This raw data is typically an array of sound waves which means almost nothing without processing.
The processing stage, or feature extraction, helps to convert raw audio data into a set of features or indicators that present information in a more understandable format to an AI model. In this stage, time-domain and frequency-domain features can be extracted. For common supervised ML applications, Mel Frequency Cepstral Coefficients (MFCCs) and Chroma features are often extracted, while advanced methods can use deep learning to learn representations directly from the raw audio waveforms.
Machine learning algorithms are used in the next stage to understand the features extracted. To achieve this, different types of algorithms can be used: classification, for identifying categories of sounds; regression, for predicting continuous values such as volume level; clustering, for grouping sounds together; and so on.
Finally, the interpreted audio data can either be acted upon or can contribute to a larger decision-making process. For instance, in voice assistants, Machine Listening is the first step that allows these assistants to recognize a user’s voice commands and react accordingly.
Machine Listening technology is used in various fields. It can play a crucial role in music information retrieval by identifying different elements of musical pieces. In healthcare, it is used to analyze respiratory sounds for diagnostic purposes. Moreover, in the environmental sector, it is used to monitor biodiversity by listening to animal sounds.
“Signal Processing”, Machine Learning (ML), Algorithms", Feature Extraction, “Classification”, Regression, Clustering, “Voice Assistant”, “Auditory Scene Analysis”, “Time-Domain Features”, “Frequency-Domain Features”, “Mel Frequency Cepstral Coefficients”, “Chroma Features”, “Music Information Retrieval”, “Computational Auditory Scene Analysis”