G. Tzanetakis, G. Essl, and P. Cook. Automatic musical genre classification of audio signals. In In Proceedings of the Int. Symposium on Music Information Retrieval (ISMIR), pages 205--210, 2001. Home/Search Document Details and Download
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An Evaluation of Alternative Feature Selection.. - Grimaldi, Cunningham, .. (2003) (Correct)
....problem with a data set of 200 music files, we find that the best classification accuracy (84 ) is achieved by the feature subspace based ensemble with the round robin ensemble also showing considerable promise. While similar music classification tasks have been tackled by other researchers [1, 3, 5] it is difficult to compare results because of the unavailability of benchmark datasets. This will continue to be a problem due to the copyright issues associated with sharing music files. The paper proceeds with an overview of the music classification problem and a very brief description of the ....
....notes and octaves. Our analysis demon strates that 9 levels of decomposition are necessary to build a spectrogram suitable for time feature extraction [6] Time features are extracted from the beat histogram. The beat histogram represents the most intense periodicities found in the signal [5]. The time features we take into account are: the intensity, the position and the width of the 20 most intensive peaks. The position of a peak is the frequency of a dominant beat, the intensity refers to the number of times a beat frequency is found in the song and the width corresponds to the ....
G. Tzanetakis, G. Essl, P. Cook, "Automatic Musical Genre Classification of Audio Signals ", In. Proc. Int. Symposium on Music Information Retrieval (ISMIR), Bloomington, Indiana, 2001.
A Query by Example Music Retrieval Algorithm - Harb, Chen (Correct)
....(mp3, WAV) For real world applications, a query by example system must be applicable to the raw audio data without any symbolic representation. Alternatively, several techniques have been proposed to find similarity between audio segments based on acoustic or psychoacoustic features [5, 6]. These techniques are, in general, based on a modified version of general audio similarity which, however, does not convey the similarity of music. In this paper we present a technique for Query by Example Music Retrieval (QEMR) based on local (ls) and global (20s) acoustic similarities in the ....
....between these musical genres. However, in average, our technique reaches a precision rate of 81 , though the objective of our technique is not the Automatic Genre Classification of music. Unfortunately, a comparison to other Automatic Genre Classification systems such as the ones described in [6, 10] is not feasible because of the lack of a common database and musical genres for testing. 4.2. Subjective evaluation In previous experiment the system s performance was evaluated based on nonsubjective judgments. For the subjective evaluation, four subjects were asked to use the system as a ....
George Tzanetakis, Georg Essl, Perry Cook, Automatic Musical Genre Classification Of Audio Signals, In Proceeding of ISMIR2001, http://ismir2001 .indiana.edu/
Figure 1: The LOUD music exploration system. - Loud An Immersive (Correct)
....We also attempted to extract genre information from CDDB, but it proved to be inaccurate, inconsistent, or missing. Genre proved to be the most difficult quality to classify, as it is almost entirely subjective. Current research attempts to automate genre classification by statistical modeling [4], but at this time, no tools currently exist to facilitate this. In the absence of an objective classification tool, we manually classified the selection of music into 21 distinct genres. In determining musical similarity, we constructed a musical graph, loosely based on the Music Maps and ....
George Tzanetakis, Georg Essl, and Perry Cook. Automatic musical genre classification of audio signals. In Proc. Int. Symposium on Music Inform. Retriev. (ISMIR), pages 205--210, Bloomington, IN, USA, October 2001.
Content-based Organization and Visualization of Music Archives - Pampalk, Rauber, Merkl (2002) (5 citations) (Correct)
....of music with a length of several minutes is not straightforward. A model of the human perceptual behavior of music using psychoacoustic findings was presented in [28] together with methods to compute the similarity of two pieces of music. A more practical approach to the topic was presented in [31] where music given as raw audio is automatically classified into genres based on musical surface and rhythm features. The rhythm features are similar to the rhythm patterns we extract, with the main difference that we analyze the rhythm in 20 frequency bands separately. Our work is part of the ....
G. Tzanetakis, G. Essl, and P. Cook. Automatic musical genre classification of audio signals. In Proc. Int. Symposium on Music Information Retrieval (SMR), 2001.
Automatic Labeling of Unpitched Percussion Sounds - Herrera, Dehamel, Gouyon (2003) (3 citations) (Correct)
.... facilities for describing sound effects have even been provided in the MPEG 7 standard [2] Usually, music streams are broadly classified according to genre, player, mood, or instrumentation, and some research has been devoted in order to automatically assign some of these labels to sound files [3], 4] Automatic labeling of instrument sounds has some obvious applications for enhancing the operating systems of sampling and synthesis devices, in order to help sound designers to categorize (or suggesting names for) new patches and samples. Automatic annotation of the instrumentation played ....
G.Tzanetakis, G.Essl, and P.Cook, "Automatic Musical Genre Classification of Audio Signals," International Symposium on Music Information Retrieval (ISMIR), 2001.
Classifying Music By Genre Using The Wavelet Packet.. - Grimaldi, Kokaram.. (2002) (2 citations) (Correct)
....binary classifiers with each classifier mined on a pair of genres. Our evaluation shows that this approach achieves very high classification accuracy. 1. INTRODUCTION In recent years, the interest of the research community in indexing multimedia data for retrieval purposes has grown considerably [1,10,11]. The requirement is to enable access to multimedia data with the same ease as textual information. For music information retrieval, a direct way to compare music tracks would allow the construction of better music browsing systems [6] or improved recommendation systems [3] In this domain, ....
....order to take into account the sampling theorem. Given music sampled at 44100 Hz, and using the Daubechies4 wavelet (Wsup = 8 taps) a maximum resolution of 300 b.p.m. and using equations (2) 3) 9 levels of decomposition are necessary. The time features are extracted from the beat histogram [10] of the signal. The histogram used here is based on the accumulation of ALL of the periodicities found in each sub band of the same graph. As indicated in Figure 1, the periodicities are found by locating peaks in the autocorrelation function of each subband. The features are: the intensity, the ....
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G. Tzanetakis, G. Essl, P. Cook, "Automatic Musical Genre Classification of Audio Signals", In. Proc. Int. Symposium on Music Information Retrieval (ISMIR), Bloomington, Indiana, 2001.
Using Psycho-Acoustic Models and Self-Organizing Maps To.. - Rauber, Pampalk, Merkl (2002) (2 citations) (Correct)
....can be regarded as the smallest possible building blocks of music. A model of the human perceptual behavior of music using psychoacoustic findings was presented in [30] together with methods to compute the similarity of two pieces of music. A more practical approach to the topic was presented in [33] where music given as raw audio is classified into genres based on musical surface and rhythm features. The features are similar to the rhythm patterns we extract, the main difference being that we analyze them separately in 20 frequency bands. Our work is based on first experiments reported in ....
G. Tzanetakis, G. Essl, and P. Cook. Automatic musical genre classification of audio signals. In Proc Int'l Symposium on Music Information Retrieval (ISMIR), Bloomington, Indiana, October 15-17 2001.
Locating Segments with Drums in Music Signals - Heittola, Klapuri (2002) (Correct)
....musical genres. For the both methods, errors occur for borderline cases with soft percussive like drum accompaniment, or transient like instrumentation without drums. 1. INTRODUCTION Segmentation and analysis of musical signals has gained increasing amounts of research interest in recent years [1,2,11,12]. The presence absence of drum instruments is an important high level descriptor for music classification and retrieval. In many cases, exactly expressible descriptors are more efficient for information retrieval than more ambiguous concepts such as musical genre. For example, someone might search ....
.... et al. used a narrowband filter at low frequencies to detect macro and micro scale periodicity [3] Tzanetakis et al. have used the Discrete Wavelet Transform to decompose the signal into a number of bands and autocorrelation function to detect the various periodicities of the signal s envelope [1]. This structure was used to extract features for musical genre classification. Soltau et al. have used HMMs with Neural Networks to represent temporal structures and variations in musical signals [2] 2. METHODS 2.1 Preprocessing with Sinusoidal Modeling Drum instruments in Western music ....
Tzanetakis,G., Essl, G. and Cook, P. Automatic musical genre classification of audio signals, In International Symposium on Musical Information Retrieval, 2001
Content-based Organization and Visualization of Music Archives - Pampalk, Rauber, Merkl (2002) (5 citations) (Correct)
....of music with a length of several minutes is not straightforward. A model of the human perceptual behavior of music using psychoacoustic findings was presented in [28] together with methods to compute the similarity of two pieces of music. A more practical approach to the topic was presented in [31] where music given as raw audio is automatically classified into genres based on musical surface and rhythm features. The rhythm features are similar to the rhythm patterns we extract, with the main di#erence that we analyze the rhythm in 20 frequency bands separately. 2 3 dB SPL Modulation ....
G. Tzanetakis, G. Essl, and P. Cook. Automatic musical genre classification of audio signals. In Proc. Int. Symposium on Music Information Retrieval (ISMIR), 2001.
Computational Models of Musical Meter Recognition - Seppänen (2001) (1 citation) (Correct)
....as 1. causal (non anticipating) models processing acoustic audio signals: Goto Muraoka [GM98] Scheirer [Sch98b] 2. noncausal models processing audio signals: Dixon [Dix01a] Foote Uchihashi [FU01] Laroche [Lar01] Muscle Fish [WBKW96] Sethares Staley [SS01] Tzanetakis Essl Cook [TEC01] and 3. models processing symbolic data: Allen Dannenberg [AD90] Brown [Bro93] Cemgil Kappen Desain Honing [CKDH01] Eck [Eck01] Large Kolen [LK94] Lee [Lee91] Parncutt [Par94] Povel Essens [PE85] Raphael [Rap01] Rosenthal [Ros92] Smith [Smi99] Temperley Sleator [TS99] and ....
....system. The beat tracker has a four band preprocessing stage consisting of octave band wavelet analysis, rectification, low pass filtering, decimation, normalization, and summation across bands. Beats are computed from this excitation signal with autocorrelation, in a noncausal fashion. TEC01] The Sethares Staley model uses the periodicity transform for beat tracking. The model is suited to processing of acoustic music signals through the pre processor, which in practice is very much alike the front end of Scheirer s model. The incoming signal is transformed to frequency domain with ....
George Tzanetakis, Georg Essl, and Perry Cook. Automatic musical genre classification of audio signals. In Proc. Int. Symposium on Music Inform. Retriev. (ISMIR), pages 205--210, Bloomington, IN, USA, October 2001.
Method Trees: Building Blocks for Self-Organizable.. - Mierswa, Morik (2005) (Correct)
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G. Tzanetakis, G. Essl, and P. Cook. Automatic musical genre classification of audio signals. In In Proceedings of the Int. Symposium on Music Information Retrieval (ISMIR), pages 205--210, 2001.
Understandable Models Of Music Collections Based On - Exhaustive Feature Generation (Correct)
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G. Tzanetakis, G. Essl, and P. Cook. Automatic musical genre classification of audio signals. In Proc. ISMIR, pages 205--210, 2001.
Tansen : A System For Automatic Raga Identification - Pandey, Mishra, Ipe (Correct)
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G. Tzanetakis, G. Essl and P. Cook: "Automatic Musical Genre Classification of Audio Signals": Proc. International Symposium of Music Information Retrieval, pp. 205-210: October 2001.
Geospatial Location of Music and Sound Files for Music.. - Mcgill Music Technology (Correct)
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G. Tzanetakis, G. Essl, and P. Cook. Automatic musical genre classification of audio signals. In International Symposium on Music Information Retrieval. n.p, 2000.
Method Trees: Building Blocks for Self-Organizable.. - Mierswa, Morik (2005) (Correct)
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G. Tzanetakis, G. Essl, and P. Cook. Automatic musical genre classification of audio signals. In In Proceedings of the Int. Symposium on Music Information Retrieval (ISMIR), pages 205--210, 2001.
Distance-based versus Tree-based Key Recognition in.. - Martens, De Meyer.. (2003) (Correct)
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G. Tzanetakis, P. Cook, G. Essl, Automatic Musical Genre Classification of Audio Signals, Proceedings of the International Symposium for Audio Information Retrieval, 2001, pp. 205--210.
Automatic Genre Classification Using Large High-Level Musical .. - McKay, Fujinaga (2004) (1 citation) (Correct)
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Tzanetakis, G., G. Essl, and P. Cook. 2001. Automatic musical genre classification of audio signals. Proceedings of the International Symposium on Music Information Retrieval. 205--10.
Techniques for the Automated Analysis of Musical Audio - Hainsworth (2003) (3 citations) (Correct)
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G. Tzanetakis, G. Essl, and P. Cook. Automatic musical genre classification of audio signals. In Proc. ISMIR, 2001.
Automatic extraction of music descriptors from acoustic.. - Zils, Pachet (2004) (Correct)
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George Tzanetakis, Georg Essl, Perry Cook. Automatic musical genre classification of audio signals. Proceedings of 2nd International Symposium on Music Information Retrieval, pp 205--210, Bloomington, IN, USA, October 2001.
Harmonic Modeling for Polyphonic Music Retrieval - Pickens (2004) (1 citation) (Correct)
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Tzanetakis, George, Essl, Georg, and Cook, Perry. Automatic musical genre classification of audio signals. In Proceedings of the 2nd Annual International Symposium on Music Information Retrieval (ISMIR) (Indiana University, Bloomington, Indiana, October 2001), J. Stephen Downie and David Bainbridge, Eds., pp. 205--210.
Music Similarity Analysis In A P2p Environment - Stephan Baumann German (2003) (1 citation) (Correct)
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G. Tzanetakis, Automatic Musical Genre Classification Of Audio Signals, IEEE Transactions on Speech and Audio Processing, July 2002. 7
Visualization of Metre and Other Rhythm Features - Guaus, Batlle (2003) (Correct)
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G. Tzanetakis; G. Essl and P. Cook, "Automatic Musical Genre Classification of Audio Signals," in Proceedings ISMIR, 2001.
Extracting Automatically The Perceived Intensity Of Music Titles - Zils, Pachet (2003) (1 citation) (Correct)
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Tzanetakis George, Essl Georg, and Cook Perry, 'Automatic musical genre classification of audio signals', in Proceedings of 2nd International Symposium on Music Information Retrieval (ISMIR01), pages 205--210, Bloomington, I N, USA, October 2001
Extracting Automatically the Perceived Intensity of Music Titles - Zils, Pachet (2003) (1 citation) (Correct)
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Tzanetakis George, Essl Georg, and Cook Perry, 'Automatic musical genre classification of audio signals', in Proceedings of 2nd International Symposium on Music Information Retrieval (ISMIR01), pages 205--210, Bloomington, IN, USA, October 2001
An Explorative, Hierarchical User Interface to Structured Music.. - Schedl (2003) (Correct)
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G. Tzanetakis, G. Essl, and P. Cook. Automatic Musical Genre Classification of Audio Signals. In Proceedings of the 2nd International Symposium on Music Information Retrieval (ISMIR'01), pages 205--210, Bloomington, Indiana, USA, 2001.
Music Similarity Measures: What's The Use ? - Aucouturier, Pachet (2002) (Correct)
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Tzanetakis, G. Automatic Musical Genre Classification of Audio Signals, in proc. ISMIR 2001.
Musical Query-by-Description as a Multiclass Learning Problem - Whitman, Rifkin (Correct)
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G. Tzanetakis, G. Essl, and P. Cook. Automatic musical genre classification of audio signals, 2001.
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