They are designed to classify/categorize sounds (sound classifier) and compose/create symphonies (virtual composer) with certain sounds, intelligently enough to pass the Turing test.įor this, we can use some audio features like Amplitude envelope, Zero-Crossing rate, and Spectral flux. For example, if we intend to find out/classify if a sound belongs to a gunshot or a motorbike engine, without any human intervention, we can use intelligent audio systems to do so. For that, we can pick a few of the audio features extracted (using signal processing algorithms), the ones that we believe, can resolve the classification problem, and feed those features to the ML algorithms. Say, we intend to resolve an audio classification problem. Intelligent audio systems are used for automatic music composition, creation, and categorization. Once we have the audio features extracted, we can use either the traditional ML algorithms or Deep Learning (DL) algorithms to build an intelligent audio system. Machine Learning and Audio Feature Extraction We use the time-frequency representation to calculate the rate of change in spectral bands of an audio signal. The frequency domain-based audio feature extraction reveals the frequency content of the audio signals, band energy ratio, and so on. The time domain-based feature extraction yields instantaneous information about the audio signals like the energy of the signal, zero-crossing rate, and amplitude envelope. The audio feature extraction from time and frequency domains is required for manipulation of the signals to remove unwanted noise and balance the time-frequency ranges. The third category is called global features and they provide information about the whole sound (algorithmic aggregate of features for the raw audio signals). These features give information about a musical phrase or lyrics. Some yield information on a 2-20 sec time frame and are called segment-level features. Some audio features yield information on a 20-100 milliseconds time frame (short chunks of audio signals) and hence are called instantaneous features. These are generally the statistical features that get extracted from the audio.
Low-Level Audio Features: These features include amplitude envelope, energy, zero-crossing rate, and so on.Mid-Level Audio Features: These features include pitch and beat level attributes such as note onsets (the start of a musical note), note fluctuation patterns, and MFCCs.High-Level Audio Features: These features include keys, chords, rhythm, melody, tempo, lyrics, genre, and mood.The audio features are divided into three categories: You can also use the extracted audio features to reduce noise and redundancy from the music files. It can help solve certain problems like classifying music files into various genres to offer context-aware music recommendations for personal use, and to build virtual composers to compose and create music. We can extract a few features of the audio signals and then pass them on to the Machine Learning (ML) algorithms to identify patterns in the audio signals. We can use these audio features to train intelligent audio systems.
Various audio features provide different aspects of the sound.
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