music genre classification




Evaluation of feature extractors and psycho-acoustic transformations for music genre classification
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We present a study on the importance of psycho-acoustic transformations for effective audio feature calculation. From the results, both crucial and problematic parts of the algorithm for Rhythm Patterns feature extraction are identified. We furthermore introduce two new featureWe report our findings on using MIDI files and audio features from MIDI, separately and combined together, for MIDI music genre classification . We use McKay and Fujinagas 3-root and 9-leaf genre data set. In order to compute distances between MIDI pieces, we use

Music Genre Classification via Compressive Sampling.
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Compressive sampling (CS) is a new research topic in signal processing that has piqued the interest of a wide range of researchers in different fields recently. In this paper, we present a CS-based classifier for music genre classification , with two sets of features, including short

Music Genre Classification Using Locality Preserving Non-Negative Tensor Factorization and Sparse Representations.
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ABSTRACT A robust music genre classification framework is proposed that combines the rich, psycho-physiologically grounded properties of auditory cortical representations of music recordings and the power of sparse representation-based classifiers. A novel

A Study on Music Genre Classification Based on Universal Acoustic Models.
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Classification of musical genres gives a useful measure of similarity and is often the most useful descriptor of a musical piece. Previous techniques to use hidden Markov models (HMMs) for automatic genre classification have used a single HMM to model an entire song

Audio content processing for automatic music genre classification : descriptors, databases, and classifiers
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This dissertation presents, discusses, and sheds some light on the problems that appear when computers try to automatically classify musical genres from audio signals. In particular, a method is proposed for the automatic music genre classification by using a computational

Improving Genre Classification by Combination of Audio and Symbolic Descriptors Using a Transcription Systems.
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ABSTRACT Recent research in music genre classification hints at a glass ceiling being reached using timbral audio features. To overcome this, the combination of multiple different feature sets bearing diverse characteristics is needed

From Multi-Labeling to Multi-Domain-Labeling: A Novel Two-Dimensional Approach to Music Genre Classification .
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In this publication we describe a novel two-dimensional approach for automatic music genre classification . Although the subject poses a well studied task in Music Information Retrieval, some fundamental issues of genre classification have not been covered so far. Especially

How Many Beans Make Five The Consensus Problem in Music - Genre Classification and a New Evaluation Method for Single-Genre Categorisation Systems.
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Genre definition and attribution is generally considered to be subjective. This makes evaluation of any genrelabelling system intrinsically difficult, as the ground-truth against which it is compared is based upon subjective responses, with little inter-participant

Learning Temporal Features Using a Deep Neural Network and its Application to Music Genre Classification .
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In this paper, we describe a framework for temporal feature learning from audio with a deep neural network, and apply it to music genre classification . To this end, we revisit the conventional spectral feature learning framework, and reformulate it in the cepstral

Audio Feature Engineering for Automatic Music Genre Classification .
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The scenarios opened by the increasing availability, sharing and dissemination of music across the Web is pushing for fast, effective and abstract ways of organizing and retrieving music material. Automatic classification is a central activity to model most of these

Multi-modal music genre classification approach
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As a fundamental and critical component of music information retrieval (MIR) systems, automatically classifying music by genre is a challenging problem. The traditional approaches which solely depending on low-level audio features not be able to obtain

Automatic music genre classification of audio signals with machine learning approaches
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Musical genre classification is put into context by explaining about the structures in music and how it is analyzed and perceived by humans. The increase of the music databases on the personal collection and the Internet has brought a great demand for music information

Music Genre Classification using Similarity Functions.
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We consider music classification problems. A typical machine learning approach is to use support vector machines with some kernels. This approach, however, does not seem to be successful enough for classifying music data in our experiments. In this paper, we follow an

Music genre classification using temporal information and support vector machine
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This paper proposes a novel content-based music genre classification method using temporal information and support vector machine. By processing of texture window statistics and delta computation, temporal information is incorporated to capture the time-varying

Improved Music Genre Classification with Convolutional Neural Networks.
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In recent years, deep neural networks have been shown to be effective in many classification tasks, including music genre classification . In this paper, we proposed two ways to improve music genre classification with convolutional neural networks: 1) combining

Automatic music genre classification for indian music
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Automatic genre classification from audio has been an area of active research due to its importance in music information retrieval systems. India is a multilingual and multi cultural country. The language and culture differ based on the geographical area, hence the music

Using block-level features for genre classification, tag classification and music similarity estimation
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[] Ballroom 89.20% MIREX 2010 Submission Ballroom 92.44% Table 1. Comparison of classification accuracies achieved by music genre classification approaches T. Li, M. Ogihara, and Q. Li. A comparative study on content-based music genre classification . In Proc

Music Genre Classification using the multivariate AR feature integration model
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Music genre classification systems are normally build as a feature extraction module followed by a classifier. The features are often short-time features with time frames of 10- 30ms, although several characteristics of music require larger time scales. Thus, larger time

A study on feature selection and classification techniques for automatic genre classification of traditional malay music.
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The rest of this paper is organised as follows: Section 2 presents background information about music genre classification and TMM 2. BACKGROUND 2.1. Music Genre Classification CSE PROJECTS

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