Audio classification is the process of listening to and analyzing audio recordings. Also known as sound classification, this process is at the heart of a variety of modern AI technology including virtual assistants, automatic speech recognition, and text to speech applications. You can also find it in predictive maintenance, smarthome security systems, and multimedia indexing and retrieval.
Audio classification projects like those mentioned above start with annotated audio data. Machines require this data to learn how to hear and what to listen for. Using this data, they develop the ability to differentiate between sounds to complete specific tasks. The annotation process often involves classifying audio files based on project-specific needs through the help of dedicated audio classification services.
In this article we look at four types of classification and related use-cases for each.
Types of Audio Classification
Acoustic Data Classification: Also known as acoustic event detection, this type of classification identifies where an audio signal was recorded. This means differentiating between environments such as restaurants, schools, homes, offices, streets, etc. One use of acoustic data classification is the building and maintaining of sound libraries for audio multimedia. It also plays a role in ecosystem monitoring. One example of this is the estimation of the abundance of fish in a particular part of the ocean based on their acoustic data.
Environmental Sound Classification: Just as the name implies, this is the classification of sounds found within different environments. For example, recognizing urban sound samples such as car horns, roadwork, sirens, human voices, etc. This is used in security systems to detect sounds like breaking glass. It is also used for predictive maintenance by detecting sound discrepancies in factory machinery. It is even used to differentiate animal calls for wildlife observation and preservation.
Music classification: Music classification is the process of classifying music based on factors such as genre or instruments played. This classification plays a key role in organizing audio libraries by genre, improving recommendation algorithms, and discovering trends and listener preferences through data analysis.
Natural Language Utterance Classification: This is the classification of natural language recordings based on language spoken, dialect, semantics, or other language features. In other words, the classification of human speech. This kind of audio classification is most common in chatbots and virtual assistants, but is also prevalent in machine translation and text to speech applications.
The Importance of Audio Data Quality
For projects involving audio classification, the quality of your dataset can and will decide the quality of your project results. Therefore, to ensure an accurate level of audio classification, you’ll need a good volume of high-quality, accurately-annotated data.
This is where we can help. Lionbridge offers a suite of voice and sound data for machine learning including audio collection, transcription, and classification. With a crowd of 500,000+ qualified workers, you can collect a diverse spread of data from a wide variety of geographic locations and language environments.