Additional Evidence That Common Low-Level Features Of Individual Audio Frames Are Not Representative Of Music Genre (original) (raw)
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Short-term Feature Space and Music Genre Classification
Journal of New Music Research, 2011
In music genre classification, most approaches rely on statistical characteristics of low-level features computed on short audio frames. In these methods, it is implicitly considered that frames carry equally relevant information loads and that either individual frames, or distributions thereof, somehow capture the specificities of each genre. In this paper we study the representation space defined by short-term audio features with respect to class boundaries, and compare different processing techniques to partition this space. These partitions are evaluated in terms of accuracy on two genre classification tasks, with several types of classifiers. Experiments show that a randomized and unsupervised partition of the space, used in conjunction with a Markov Model classifier lead to accuracies comparable to the state of the art. We also show that unsupervised partitions of the space tend to create less hubs.
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.
Automatic genre classification using large high-level musical feature sets
2004
This paper presents an automatic description system of drum sounds for real-world musical audio signals. Our system can represent onset times and names of drums by means of drum descriptors defined in the context of MPEG-7. For their automatic description, drum sounds must be identified in such polyphonic signals. The problem is that acoustic features of drum sounds vary with each musical piece and precise templates for them cannot be prepared in advance. To solve this problem, we propose new template-adaptation and template-matching methods. The former method adapts a single seed template prepared for each kind of drums to the corresponding drum sound appearing in an actual musical piece. The latter method then can detect all the onsets of each drum by using the corresponding adapted template. The onsets of bass and snare drums in any piece can thus be identified. Experimental results showed that the accuracy of identifying bass and snare drums in popular music was about 90%. Finally, we define drum descriptors in the MPEG-7 format and demonstrate an example of the automatic drum sound description for a piece of popular music.
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.
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.
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.
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.
Music Genre Classification with the Million Song Dataset
2011
The field of Music Information Retrieval (MIR) draws from musicology, signal processing, and artificial intelligence. A long line of work addresses problems including: music understanding (extract the musically-meaningful information from audio waveforms), automatic music annotation (measuring song and artist similarity), and other problems. However, very little work has scaled to commercially sized data sets. The algorithms and data are both complex.
Factors in automatic musical genre classification of audio signals
2003
Abstract Automatic musical genre classification is an important tool for organizing the large collections of music that are becoming available to the average user. In addition, it provides a structured way of evaluating musical content features that does not require extensive user studies. The paper provides a detailed comparative analysis of various factors affecting automatic classification performance, such as choice of features and classifiers.
Improving automatic music genre classification with hybrid content-based feature vectors
Proceedings of the 2010 ACM Symposium on Applied Computing - SAC '10, 2010
Current research on the task of automatic music genre classification has been focusing on new classification approaches based on combining information from other sources than the music signal. The reason for this is that the use of contentbased approaches, i.e. using features extracted directly from the audio signal, seems to have reached a glass ceiling. In this work we show that by using different types of contentbased features together it is possible to substantially improve the classification accuracy. This is an interesting result as different types of content-based features aim, at a conceptual level, to capture the same type of information. In order to identify which types of content-based features are responsible for the predictive accuracy gain, we also used a feature selection (FS) approach based on a genetic algorithm (GA). The analysis of the results in two databases shows that the use of the GA for FS succeeds in selecting a representative subset without significant loss in accuracy. It also shows that all the different types of content-based features employed are important for the improvement of the accuracy in classifying music genres.