G. Tzanetakis and P. Cook, "Audio information retrieval (AIR) tools," in Proc. Int. Symposium on Audio Information Retrieval, ISMIR, 2000. Home/Search Document Details and Download
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Music Database Retrieval Based on Spectral Similarity - Yang (2001) (1 citation) (Correct)
....is generally sufficient for retrieval purposes [2, 5, 6] Among music retrieval research conducted on raw audio databases, Scheirer [7, 8] studied pitch and rhythmic analysis, segmentation, as well as music similarity estimation at a high level such as genre classification. Tzanetakis and Cook [10] built tools to distinguish speech from music, and to do segmentation and simple retrieval tasks. Wold et al. at Muscle Fish LLC [11] developed audio retrieval methods for a wider range of sounds besides music, based on analyses of sound signals statistical properties such as loudness, pitch, ....
G. Tzanetakis and P. Cook, "Audio Information Retrieval (AIR) Tools", in International Symposium on Music Information Retrieval, 2000.
Soundspotter -- A Prototype System For Content-Based.. - Christian Spevak Faculty (2002) (4 citations) (Correct)
....current implementation of our system is a prototype designed to evaluate different retrieval algorithms. Our research fits into the emerging discipline of audio information retrieval [1, 2] Related work in this area includes content based retrieval and browsing of sound files in audio databases [3, 4, 5], audio classification [6, 7] and sound source recognition [8] Soundspotter is based on a modular architecture consisting of four stages. First, a frame based feature extraction is carried out using mel frequency cepstral coefficients (MFCCs) Second, the feature vectors are clustered or mapped ....
George Tzanetakis and Perry Cook, "Audio information retrieval (AIR) tools," In ISMIR [23].
Music Database Retrieval Based on Spectral Similarity - Yang (2001) (1 citation) (Correct)
....Similarity is based on the intuitive notion of similarity perceived by humans: two pieces are similar if they are fully or partially based on the same score, even if they are performed by different people or at different tempo. See our full paper [12] for a detailed review of other related work [1, 3, 4, 7, 8, 9, 10, 11, 14]. 2 The Algorithm The algorithm consists of three components, which are discussed below. 1. Intermediate Data Generation. For each music piece, we generate its spectrogram, and plot its instantaneous power as a function of time. Next, we identify peaks in this power plot, where peak is defined ....
G. Tzanetakis and P. Cook, "Audio Information Retrieval (AIR) Tools", in International Symposium on Music Information Retrieval, 2000.
Marsyas3d: A Prototype Audio Browser-Editor - Using Large Scale (2001) Self-citation (Tzanetakis Cook) (Correct)
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G. Tzanetakis and P. Cook, "Audio information retrieval (AIR) tools," in Proc. Int. Symposium on Audio Information Retrieval, ISMIR, 2000.
Summarizing Popular Music Via Structural Similarity Analysis - Matthew Cooper And (Correct)
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G. Tzanetakis and P. Cook. "Audio Information Retrieval (AIR) Tools," Proc. International Symposium on Music Information Retrieval, 2000.
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