AI for Audio Analysis

How do we use AI to extract useful information from audio?

Audio is how we hear the world and speak to one another. Audio captures the sounds that we hear and the words that we speak. As a result, audio is essential to our understanding of the world around us.

In the last two articles of this multi-part series on The AI Developer’s Toolkit, I introduced you to the top AI tools for text analysis and text synthesis. In this article, I’ll introduce you to the three most popular AI tools for audio analysis.

Sound Classification

Sound classification allows us to assign a sound to two or more labeled categories. It answers the question, “what kind of sound is this?”

For example, we can use sound classification to determine which type of animal produced a specific type of noise or vocalization. We provide the sound-classification model with an audio sample. Then the model produces a predicted category for the sound as output.

Sound classification is useful anytime we are trying to assign sounds to two or more categories. For example:

• detecting gunshots in audio-surveillance systems
• predicting mechanical failure from acoustical anomalies
• identifying species of animals for wildlife conservation

Speaker Recognition

Speaker recognition allows us to use the sound of someone’s voice to identify the speaker. It answers the question “whose voice is this?”

For example, we can use speaker recognition to determine who is speaking in an audio recording. We provide the speaker-recognition model with a sample of a human voice as input. Then the model produces the identity of the speaker and a confidence score as output.

Speaker recognition is useful anytime you need to know whose voice is speaking. For example:

• authorizing the user for voice-controlled devices
• personalizing voice responses based on who is speaking
• identifying who is speaking each line of dialog in a movie

Speech Recognition

Speech recognition allows us to convert spoken words into a string of text. It answers the question, “what is being said here?”

For example, we can use speech recognition to convert spoken dialog into a written transcript. We provide the speech-recognition model with an audio recording as input. Then the model produces the corresponding text as output.

Speech recognition is useful anytime you need to convert spoken words into text for processing. For example:

• controlling computers via voice commands
• dictating spoken words into a written document
• adding closed-captions for the dialog in a video

Other Tools

Beyond these three key examples there are also a variety of other audio-analysis tools. For example:

• Sound detection – which identifies the beginning and end of each sound event in a noisy environment
• Sound localization – which locates the source of a sound in a three-dimensional space
• Song recognition – which identifies a song based on a short audio snippet

As we can see audio-analysis tools allow us to extract useful information from digital audio.


If you’d like to learn how to use all of the tools listed above, please watch my online course: The AI Developer’s Toolkit.

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