Automatic genre classification using large high-level musical feature sets (original) (raw)
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Proceedings of the International Computer Music …, 2004
This paper examines the potential of high-level features extracted from symbolic musical representations in regards to musical classification. Twenty features are implemented and tested by using them to classify 225 MIDI files by genre. This system differs from previous automatic genre classification systems, which have focused on low-level features extracted from audio data. Files are classified into three parent genres and nine sub-genres, with average success rates of 84.8% for the former and 57.8% for the latter. Classification is performed by a novel configuration of feed-forward neural networks that independently classify files by parent genre and sub-genre and combine the results using weighted averages.
Music genre classification using midi and audio features
EURASIP Journal on Applied …, 2007
We 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 Fujinaga's 3-root and 9-leaf genre data set. In order to compute distances between MIDI pieces, we use normalized compression distance (NCD). NCD uses the compressed length of a string as an approximation to its Kolmogorov complexity and has previously been used for music genre and composer clustering. We convert the MIDI pieces to audio and then use the audio features to train different classifiers. MIDI and audio from MIDI classifiers alone achieve much smaller accuracies than those reported by McKay and Fujinaga who used not NCD but a number of domain-based MIDI features for their classification. Combining MIDI and audio from MIDI classifiers improves accuracy and gets closer to, but still worse, accuracies than McKay and Fujinaga's. The best root genre accuracies achieved using MIDI, audio, and combination of them are 0.75, 0.86, and 0.93, respectively, compared to 0.98 of McKay and Fujinaga. Successful classifier combination requires diversity of the base classifiers. We achieve diversity through using certain number of seconds of the MIDI file, different sample rates and sizes for the audio file, and different classification algorithms.
A Music genre classification using music features and neural networks
2009
Proceeding of the 5th International Conference on Information & Communication Technology and Systems : 15-20 ; Department of Informations Faculty of Information Technology ITS Surabaya 2009, 4 AugustGenre is a conventional way to classify music. This paper aims to implement an algorithm to classify music files into four different genre, which are classic, rock, pop, and dangdut. Dangdut is Indian influenced Indonesian popular music. An automatic music genre classification system will help music collectors to classify their music collection. The genre classification algorithm is divided into music feature extractor and classifier. The feature extraction of the system is constructed by timbre feature extraction and rhythm feature extraction. The timbre feature extraction is done by mel frequency cepstral coefficients (MFCC). The rhythm feature is constructed by full wave rectification, low pass filtering, down sampling, mean removal, and autocorrelation. The classifier is formed by ba...
Musical genre classification of audio signals
Speech and Audio Processing, IEEE …, 2002
Musical genres are categorical labels created by humans to characterize pieces of music. A musical genre is characterized by the common characteristics shared by its members. These characteristics typically are related to the instrumentation, rhythmic structure, and harmonic content of the music. Genre hierarchies are commonly used to structure the large collections of music available on the Web. Currently musical genre annotation is performed manually. Automatic musical genre classification can assist or replace the human user in this process and would be a valuable addition to music information retrieval systems. In addition, automatic musical genre classification provides a framework for developing and evaluating features for any type of content-based analysis of musical signals.
A hierarchical approach to automatic musical genre classification
Proc. 6th Int. Conf. on Digital Audio Effects’ 03, 2003
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 large number of audio features are evaluated for their suitability in such a classification task, including well-known physical and perceptual features, audio descriptors defined in the MPEG-7 standard, as well as new features proposed in this work. These are selected with regard to their ability to distinguish between a given set of audio ...
Automatic Musical Genre Classification of Audio Signals
Proceedings of the 2nd International Symposium on …, 2001
Musical genres are categorical descriptions that are used to describe music. They are commonly used to structure the increasing amounts of music available in digital form on the Web and are important for music information retrieval. Genre categorization for audio has traditionally been performed manually. A particular musical genre is characterized by statistical properties related to the instrumentation, rhythmic structure and form of its members. In this work, algorithms for the automatic genre categorization of audio signals are described. More specifically, we propose a set of features for representing texture and instrumentation. In addition a novel set of features for representing rhythmic structure and strength is proposed. The performance of those feature sets has been evaluated by training statistical pattern recognition classifiers using real world audio collections. Based on the automatic hierarchical genre classification two graphical user interfaces for browsing and interacting with large audio collections have been developed.
Automatic Genre Classification of Musical Signals
Eurasip Journal on Advances in Signal Processing, 2007
We present a strategy to perform automatic genre classification of musical signals. The technique divides the signals into 21.3 milliseconds frames, from which 4 features are extracted. The values of each feature are treated over 1-second analysis segments. Some statistical results of the features along each analysis segment are used to determine a vector of summary features that characterizes the respective segment. Next, a classification procedure uses those vectors to differentiate between genres. The classification procedure has two main characteristics: (1) a very wide and deep taxonomy, which allows a very meticulous comparison between different genres, and (2) a wide pairwise comparison of genres, which allows emphasizing the differences between each pair of genres. The procedure points out the genre that best fits the characteristics of each segment. The final classification of the signal is given by the genre that appears more times along all signal segments. The approach has shown very good accuracy even for the lowest layers of the hierarchical structure.
Automatic music classification into genres
Musical genres are categorical labels created by humans to characterize pieces of music. Although music genres are inexact and can often be quite arbitrary and controversial, it is believed that certain song characteristics like instrumentation, rhythmic structure, and harmonic content of the music are related to the genre. In this paper, the task of automatic music genre classification is explored. Multiple features based on timbral texture, rhythmic content and pitch content are extracted from a single music piece and used to train different classifiers for genre prediction. The experiments were performed using features extracted from one or two 30 second segments from each song. For the classification, two different architectures flat and hierarchical classification and three different classifiers (kNN, MLP and SVM) were tried. The experiments were performed on the full feature set (316 features) and on a PCA reduced feature set. The testing speed of the classifiers was also measured.The experiments carried out on a large dataset containing more than 1700 music samples from ten different music genres have shown accuracy of 69.1% for the flat classification architecture (utilizing one against all SVM based classifiers). The accuracy obtained using the hierarchical classification architecture was slightly lower 68.8%, but four times faster than the flat architecture.
A Machine Learning Approach to Automatic Music Genre Classification
Journal of The Brazilian Computer Society, 2008
This paper presents a non-conventional approach for the automatic music genre classification problem. The proposed approach uses multiple feature vectors and a pattern recognition ensemble approach, according to space and time decomposition schemes. Despite being music genre classification a multi-class problem, we accomplish the task using a set of binary classifiers, whose results are merged in order to produce the final music genre label (space decomposition). Music segments are also decomposed according to time segments obtained from the beginning, middle and end parts of the original music signal (time-decomposition). The final classification is obtained from the set of individual results, according to a combination procedure. Classical machine learning algorithms such as Naïve-Bayes, Decision Trees, k Nearest-Neighbors, Support Vector Machines and MultiLayer Perceptron Neural Nets are employed. Experiments were carried out on a novel dataset called Latin Music Database, which contains 3,160 music pieces categorized in 10 musical genres. Experimental results show that the proposed ensemble approach produces better results than the ones obtained from global and individual segment classifiers in most cases. Some experiments related to feature selection were also conducted, using the genetic algorithm paradigm. They show that the most important features for the classification task vary according to their origin in the music signal.