Classification of audio signals using statistical features on time and wavelet transform domains

Lambrou, Tryphon; Kudumakis, Panos; Speller, Robert; Sandler, Mark; Linney, AD

doi:10.1109/icassp.1998.679665

Cited by 78 publications

(48 citation statements)

References 6 publications

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“…Many different methods have been tried, including Fourier analysis and related measures such as cepstral analysis (yielding MFCCs). In addition to the family of Fourier methods, results have been reported for wavelet analysis (Lambrou et al, 1998), autoregression (Ahrendt and Meng, 2005) and the collection of statistics such as zero-crossing rate, spectral centroid, spectral roll-off and spectral spread. A survey by Aucouturier and Pachet (2003) describes a number of popular features for music similarity and classification, and research continues (e.g.…”

Section: Feature Extraction and Aggregationmentioning

confidence: 99%

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Aggregate features and ADABOOST for music classification

et al. 2006

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We present an algorithm that predicts musical genre and artist from an audio waveform. Our method uses the ensemble learner ADABOOST to select from a set of audio features that have been extracted from segmented audio and then aggregated. Our classifier proved to be the most effective method for genre classification at the recent MIREX 2005 international contests in music information extraction, and the second-best method for recognizing artists. This paper describes our method in detail, from feature extraction to song classification, and presents an evaluation of our method on three genre databases and two artist-recognition databases. Furthermore, we present evidence collected from a variety of popular features and classifiers that the technique of classifying features aggregated over segments of audio is better than classifying either entire songs or individual short-timescale features.

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Section: Feature Extraction and Aggregationmentioning

confidence: 99%

“…These include minimum distance and K-nearest neighbor in Lambrou et al (1998), and Logan and Salomon (2001). used Gaussian mixtures.…”

Section: Classificationmentioning

confidence: 99%

Aggregate features and ADABOOST for music classification

et al. 2006

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“…Tzanetakis and Cook [31] proposes a comprehensive set of features for direct modeling of music signals and explores the use of those features for musical genre classification using K-Nearest Neighbors and Gaussian Mixture models. Lambrou et al [14] uses statistical features in the temporal domain as well as three different wavelet transform domains to classify music into rock, piano and jazz. Deshpande et al [5] uses Gaussian Mixtures, Support Vector Machines and Nearest Neighbors to classify the music into rock, piano, and jazz based on timbral features.…”

Section: Related Workmentioning

confidence: 99%

A comparative study on content-based music genre classification

Li¹,

Ogihara²,

Li³

2003

Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval - SIGIR

107

152

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Content-based music genre classification is a fundamental component of music information retrieval systems and has been gaining importance and enjoying a growing amount of attention with the emergence of digital music on the Internet. Currently little work has been done on automatic music genre classification, and in addition, the reported classification accuracies are relatively low. This paper proposes a new feature extraction method for music genre classification, DWCHs 1 . DWCHs capture the local and global information of music signals simultaneously by computing histograms on their Daubechies wavelet coefficients. Effectiveness of this new feature and of previously studied features are compared using various machine learning classification algorithms, including Support Vector Machines and Linear Discriminant Analysis. It is demonstrated that the use of DWCHs significantly improves the accuracy of music genre classification.

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“…Lambrou et al (19) use the Daubechies 4-TAP wavelet filter in all wavelet architectures. From all the signals and their wavelet transformation coefficients, they collect the first and second order statistical values, as well as the grey level run length measurements.…”

Section: Multiscale Texture Features Extraction -Wavelet Based Methodmentioning

confidence: 99%

Classification algorithms for microcalcifications in mammograms (Review)

Sakka

Prentza²,

Koutsouris³

2006

Oncol Rep

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Abstract. Early detection is the key to improve breast cancer prognosis. The only proven effective method of breast cancer early detection is mammography. An early sign of 30-50% of breast cancer is the appearance of clusters of fine, granular microcalcifications and 60-80% of breast carcinomas reveal microcalcification clusters upon histological examination. The high correlation between the appearance of the microcalcification clusters and diseases, proves that computer aided diagnosis (CAD) systems for automated classification of microcalcification clusters will be very useful and helpful for breast cancer control. The fuzzy nature of microcalcification, the low contrast and the low ability of distinguishing them from their surroundings make automated characterization of them extremely difficult. In this study, we give an overview of the currently available literature on characterization of malignant and benign microcalcifications. We compare and evaluate some of the classification algorithms on microcalcifications in mammograms used in various CAD systems, which are separated into categories according to the method in use. Neural networks are used in applications where only a few decisions are required concerning an amount of data. The K-nearest neighbour classifier distinguishes unknown patterns based on the similarity to known samples and the decision tree approach is much simpler than neural networks and does not need extensive knowledge of the probability distribution of the features.

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Classification of audio signals using statistical features on time and wavelet transform domains

Cited by 78 publications

References 6 publications

Aggregate features and ADABOOST for music classification

Aggregate features and ADABOOST for music classification

A comparative study on content-based music genre classification

Classification algorithms for microcalcifications in mammograms (Review)

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