Audio Analysis Applications for Music

The last decade has seen a revolution in the use of digital audio: The CD, which one decade earlier had taken over the home audio market, is starting to be replaced by electronic media which are distributed over the Internet and stored on computers or portable devices in compressed formats. The need has arisen for software to manage and manipulate the gigabytes of data in these music collections, and with the continual increase in computer speed, memory and disk storage capacity, the development of many previously infeasible applications has become possible. This article provides a brief review of automatic analysis of digital audio recordings with musical content, a rapidly expanding research area which finds numerous applications. One application area is the field of music information retrieval, where content-based indexing, classification and retrieval of audio data are needed in order to manage multimedia databases and libraries, as well as being useful in music retailing and commercial information services. Another application area is music software for the home and studio, where automatic beat tracking and transcription of music are much desired goals. In systematic musicology, audio analysis algorithms are being used in the study of expressive interpretation of music. Other emerging applications which make use of audio analysis are music recommender systems, playlist generators, visualisation systems, and software for automatic synchronisation of audio with other media and/or devices. We illustrate recent developments with three case studies of systems which analyse specific aspects of music (Dixon, 2004). The first system is BeatRoot (Dixon, 2001a, 2001c), a beat tracking system that finds the temporal location of musical beats in an audio recording, analogous to the way that people tap their feet in time to music. The second system is JTranscriber, an interactive automatic transcription system based on (Dixon, 2000a, 2000b), which recognizes musical notes and converts them into MIDI format, displaying the audio data as a spectrogram with the MIDI data overlaid in piano roll notation, and allowing interactive monitoring and correction of the extracted MIDI data. The third system is the Performance Worm (Dixon, Goebl, & Widmer, 2002), a real-time system for visualisation of musical expression, which presents in real time a two dimensional animation of variations in tempo and loudness (Langner & Goebl, 2002, 2003). Space does not permit the description of the many other music content analysis applications, such as: audio fingerprinting, where recordings can be uniquely identified with a high degree of accuracy, even with poor sound quality and in noisy environments (Wang, 2003); music summarisation, where important parts of songs such as choruses are identified automatically; instrument identification, using machine learning techniques to classify sounds by their source instruments; and melody and bass line extraction, essential components of query-by-example systems, where music databases can be searched by singing or whistling a small part of the desired piece. At the end of the article, we discuss emerging and future trends and research opportunities in audio content analysis.