Beat histogram features for rhythm-based musical genre classification using multiple novelty functions

@inproceedings{Lykartsis2015BeatHF,
  title={Beat histogram features for rhythm-based musical genre classification using multiple novelty functions},
  author={Athanasios Lykartsis and Alexander Lerch},
  year={2015}
}
In this paper we present beat histogram features for multiple level rhythm description and evaluate them in a musical genre classification task. Audio features pertaining to various musical content categories and their related novelty functions are extracted as a basis for the creation of beat histograms. The proposed features capture not only amplitude, but also tonal and general spectral changes in the signal, aiming to represent as much rhythmic information as possible. The most and least… Expand
Rhythm Description for Music and Speech Using the Beat Histogram with Multiple Novelty Functions : First Results
In the last few years, methods for rhythmic analysis of music signals have become widespread in their use due to their value for diverse tasks of music processing. In the field of Music InformationExpand
On the analysis of speech rhythm for language and speaker identification
TLDR
This paper presents how an automatic rhythm extraction method borrowed from music information retrieval, the beat histogram, can be adapted for the analysis of speech rhythm by defining the most relevant novelty functions in the speech signal and extracting features describing their periodicities. Expand
Comparative Analysis of Three Improved Deep Learning Architectures for Music Genre Classification
TLDR
This study holds a comparative analysis of the three most impressive architectural variants of the main deep learning models that are prominently used to classify music genre and presents the three architecture, hence the models (CNN, RNN, and CRNN) in one study. Expand
Bottom-up Broadcast Neural Network For Music Genre Classification
TLDR
The proposed CNN architecture takes the long contextual information into considerations, which transfers more suitable information for the decision-making layer, and develops a novel CNN architecture for music genre classification. Expand
Pattern analysis based acoustic signal processing: a survey of the state-of-art
TLDR
The aim of this state-of-art paper is to produce a summary and guidelines for using the broadly used methods, to identify the challenges as well as future research directions of acoustic signal processing. Expand
Speaker Identification for Swiss German with Spectral and Rhythm Features
TLDR
The paper focus is the evaluation on one corpus (swiss german, TEVOID) using support vector machines, suggesting that the general spectral features can provide very good performance on this dataset, whereas the rhythm features are not as successful in the task, indicating either the lack of suitability for this task or the dataset specificity. Expand
Audio Content Analysis
TLDR
This chapter focuses on music signals, where ACA is often referred to as Music Information Retrieval (MIR) [84, 14], although the latter additionally encompasses the analysis and generation of symbolic (non-audio) music data such as musical scores. Expand
Transfer Learning for Music Classification and Regression Tasks
TLDR
This paper proposes to use a pre-trained convnet feature, a concatenated feature vector using the activations of feature maps of multiple layers in a trained convolutional network, and shows how it can serve as general-purpose music representation. Expand
High-Level Music Descriptor Extraction Algorithm Based on Combination of Multi-Channel CNNs and LSTM
TLDR
Experimental results demonstrate that, when compared with the hand-crafted schemes or conventional deep learning, MCCLSTM achieves higher prediction accuracy on three music collections with different kinds of semantic tags. Expand
Preliminary Investigation for Tamil cine music deployment for mood music recommender system
TLDR
Preliminary results of the signal processing module and machine learning module with four songs in detail and with a database of 100 songs is carried out, suggesting improvement in the features and better machine learning algorithm before porting to Android for development as a Mobile App. Expand
...
1
2
...

References

SHOWING 1-10 OF 59 REFERENCES
Beat Histogram Features from NMF-Based Novelty Functions for Music Classification
TLDR
Novel rhythm features derived from drum tracks extracted from polyphonic music and evaluated in a genre classification task show that the presented NMF-based beat histogram features can provide comparable performance to other classification systems, while considering strictly drum patterns. Expand
Towards Characterisation of Music via Rhythmic Patterns
TLDR
A new method of characterising music by typical bar-length rhythmic patterns which are automatically extracted from the audio signal is presented, and the usefulness of this representation by its application in a genre classification task is demonstrated. Expand
Musical genre classification of audio signals
TLDR
The automatic classification of audio signals into an hierarchy of musical genres is explored and three feature sets for representing timbral texture, rhythmic content and pitch content are proposed. Expand
A HIERARCHICAL APPROACH TO AUTOMATIC MUSICAL GENRE CLASSIFICATION
A system for the automatic classification of audio signals according to audio category is presented. The signals are recognized as speech, background noise and one of 13 musical genres. A largeExpand
Using the beat histogram for speech rhythm description and language identification
TLDR
This paper presents how an automatic rhythm extraction method borrowed from music information retrieval, the beat histogram, can be adapted for the analysis of speech rhythm by defining the most relevant novelty functions in the speech signal and extracting features describing their periodicities. Expand
Classification of dance music by periodicity patterns
TLDR
This paper compares two methods of extracting periodicities from audio recordings to find the metrical hierarchy and timing patterns by which the style of the music can be recognised, and uses autocorrelation on the amplitude envelopes of band-limited versions of the signal as its method of periodicity detection. Expand
Evaluating Rhythmic descriptors for Musical Genre Classification
TLDR
This article considers a specific set of rhythmic descriptors for which it provides procedures of automatic extraction from audio signals and concludes on the particular relevance of the tempo and a set of 15 MFCC-like descriptors. Expand
The beat spectrum: a new approach to rhythm analysis
TLDR
The beat spectrum is a measure of acoustic self-similarity versus lag time, computed from a representation of spectrally similarity, which has a variety of applications, including music retrieval by similarity and automatically generating music videos. Expand
Spectral and Temporal Periodicity Representations of Rhythm for the Automatic Classification of Music Audio Signal
  • G. Peeters
  • Mathematics, Computer Science
  • IEEE Transactions on Audio, Speech, and Language Processing
  • 2011
TLDR
This paper test the use of the periodicity representations alone, combined with tempo information and combined with a proposed set of rhythm features to achieve high recognition rates at least comparable to previously published results. Expand
HUMAN PERCEPTION AND COMPUTER EXTRACTION OF MUSICAL BEAT STRENGTH
TLDR
In the work presented in this paper, a user study exploring the perception of Beat Strength was conducted and the results were used to calibrate and explore automatic Beat Strength measures based on the calculation of Beat Histograms. Expand
...
1
2
3
4
5
...