Automatic Classification of Audio Data (original) (raw)
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Combination of homogeneous classifiers for musical genre classification
Content-based music genre classification is a useful tool for multimedia indexing and retrieval. In this paper a novel content-based music genre classification approach that employs combination of homogeneous classifiers is proposed. First, musical surface features and beat-related features are extracted from different pans of music tracks and three 15-dimensional feature vectors are generated. The features are extracted from the beginning, middle and end parts of the music. These features vectors are used to train three multilayer perceptron neural network classifiers. At the classification step, the outputs provided by each neural network based classifier are combined using max, sum and product rules. Experimental results show that the proposed combination of homogeneous classifiers outperforms single feature vectors and single classifiers, achieving higher correct music genre classification rates.
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.
Automatic music genre classification using ensemble of classifiers
IEEE International Conference on Systems, Man, and Cybernetics, 2007
This paper presents a novel approach to the task of automatic music genre classification which is based on multiple feature vectors and ensemble of classifiers. Multiple feature vectors are extracted from a single music piece. First, three 30-second music segments, one from the beginning, one from the middle and one from end part of a music piece are selected and feature vectors are extracted from each segment. Individual classifiers are trained to account for each feature vector extracted from each music segment. At the classification, the outputs provided by each individual classifier are combined through simple combination rules such as majority vote, max, sum and product rules, with the aim of improving music genre classification accuracy. Experiments carried out on a large dataset containing more than 3,000 music samples from ten different Latin music genres have shown that for the task of automatic music genre classification, the features extracted from the middle part of the music provide better results than using the segments from the beginning or end part of the music. Furthermore, the proposed ensemble approach, which combines the multiple feature vectors, provides better accuracy than using single classifiers and any individual music segment.
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.
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.
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.
2017
─ With the advancement of technology in today’s era, there is an utmost need for reliable music retrieval methods in order to organize and search through the large music archives that are available on the internet. Music genre classification is the most fundamental and essential component in music information retrieval (MIR) systems. An appropriate choice of music features and classifier is a crucial task for developing an accurate and efficient contentbased classification system. In this work, a comparative analysis for four different set of features, viz. dynamic, timbretexture, pitch and tonal features along with the statistical parameters is examined based on the performance of respective feature set. The performance evaluation is carried out on GTZAN musical database by using support vector machine (SVM) as a classifier. The experimental results show that out of all four set of features, better classification accuracy of 95.77% is achieved for dynamic and timbre texture features.
A Study on Music Genre Classification using Machine Learning
International Journal of Engineering Business and Social Science
Artificial Intelligence (AI) and Machine Learning can be cited as one of the greatest technological advancements in this century. They are revolutionizing the fields of computing, finance, healthcare, agriculture, music, space and tourism. Powerful models have achieved excellent performance on a myriad of complex learning tasks. One such subset of AI is audio analysis. It entails music information retrieval, music generation and music classification. Music data is one the most abstruse type of source data present, mainly because it is a tough work to extract meaningful correlating features from it. Hence a myriad of algorithms ranging from classical to hybrid neural networks have been tried on music data for a getting a good accuracy. This paper studies the various methods that can be used for music genre classification and compares between them. The accuracies we obtained on a small sample of the Free Music Archive (FMA) dataset were: 46% using Support Vector Classifier (SVC), 40% ...
Audio feature engineering for automatic music genre classification
2007
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 processes, thus its design plays a relevant role in advanced Music Information Retrieval. In this paper, we adopted a state-of-the-art machine learning algorithm, i.e. Support Vector Machines, to design an automatic classifier of music genres. In order to optimize classification accuracy, we implemented some already proposed features and engineered new ones to capture aspects of songs that have been neglected in previous studies. The classification results on two datasets suggest that our model based on very simple features reaches the state-of-art accuracy (on the ISMIR dataset) and very high performance on a music corpus collected locally.
Feature Selection in Automatic Music Genre Classification
2008 Tenth IEEE International Symposium on Multimedia, 2008
This paper presents the results of the application of a feature selection procedure to an automatic music genre classification system. The classification system is based on the use of multiple feature vectors and an ensemble approach, according to time and space decomposition strategies. Feature vectors are extracted from music segments from the beginning, middle and end of the original music signal (timedecomposition). Despite being music genre classification a multi-class problem, we accomplish the task using a combination of binary classifiers, whose results are merged in order to produce the final music genre label (space decomposition). As individual classifiers several machine learning algorithms were employed: Naïve-Bayes, Decision Trees, Support Vector Machines and Multi-Layer Perceptron Neural Nets. Experiments were carried out on a novel dataset called Latin Music Database, which contains 3,227 music pieces categorized in 10 musical genres. The experimental results show that the employed features have different importance according to the part of the music signal from where the feature vectors were extracted. Furthermore, the ensemble approach provides better results than the individual segments in most cases.