Music genre classification via joint sparse low-rank representation of audio features

A novel framework for music genre classification, namely the joint sparse low-rank representation (JSLRR) is proposed in order to: 1) smooth the noise in the test samples, and 2) identify the subspaces that the test samples lie onto. An efficient algorithm is proposed for obtaining the JSLRR and a novel classifier is developed, which is referred to as the JSLRR-based classifier. Special cases of the JSLRR-based classifier are the joint sparse representation-based classifier and the low-rank representation-based one. The performance of the three aforementioned classifiers is compared against that of the sparse representation-based classifier, the nearest subspace classifier, the support vector machines, and the nearest neighbor classifier for music genre classification on six manually annotated benchmark datasets. The best classification results reported here are comparable with or slightly superior than those obtained by the state-of-the-art music genre classification methods

Music classification by low-rank semantic mappings

A challenging open question in music classification is which music representation (i.e., audio features) and which machine learning algorithm is appropriate for a specific music classification task. To address this challenge, given a number of audio feature vectors for each training music recording that capture the different aspects of music (i.e., timbre, harmony, etc.), the goal is to find a set of linear mappings from several feature spaces to the semantic space spanned by the class indicator vectors. These mappings should reveal the common latent variables, which characterize a given set of classes and simultaneously define a multi-class linear classifier that classifies the extracted latent common features. Such a set of mappings is obtained, building on the notion of the maximum margin matrix factorization, by minimizing a weighted sum of nuclear norms. Since the nuclear norm imposes rank constraints to the learnt mappings, the proposed method is referred to as low-rank semantic mappings (LRSMs). The performance of the LRSMs in music genre, mood, and multi-label classification is assessed by conducting extensive experiments on seven manually annotated benchmark datasets. The reported experimental results demonstrate the superiority of the LRSMs over the classifiers that are compared to. Furthermore, the best reported classification results are comparable with or slightly superior to those obtained by the state-of-the-art task-specific music classification methods

Music classification by low-rank semantic mappings

A challenging open question in music classification is which music representation (i.e., audio features) and which machine learning algorithm is appropriate for a specific music classification task. To address this challenge, given a number of audio feature vectors for each training music recording that capture the different aspects of music (i.e., timbre, harmony, etc.), the goal is to find a set of linear mappings from several feature spaces to the semantic space spanned by the class indicator vectors. These mappings should reveal the common latent variables, which characterize a given set of classes and simultaneously define a multi-class linear classifier that classifies the extracted latent common features. Such a set of mappings is obtained, building on the notion of the maximum margin matrix factorization, by minimizing a weighted sum of nuclear norms. Since the nuclear norm imposes rank constraints to the learnt mappings, the proposed method is referred to as low-rank semantic mappings (LRSMs). The performance of the LRSMs in music genre, mood, and multi-label classification is assessed by conducting extensive experiments on seven manually annotated benchmark datasets. The reported experimental results demonstrate the superiority of the LRSMs over the classifiers that are compared to. Furthermore, the best reported classification results are comparable with or slightly superior to those obtained by the state-of-the-art task-specific music classification methods

Unsupervised automatic music genre classification

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia InformáticaIn this study we explore automatic music genre recognition and classification of digital music. Music has always been a reflection of culture di erences and an influence in our society. Today’s digital content development triggered the massive use of digital music. Nowadays,digital music is manually labeled without following a universal taxonomy, thus, the labeling process to audio indexing is prone to errors. A human labeling will always be influenced by culture di erences, education, tastes, etc. Nonetheless, this indexing process is primordial to guarantee a correct organization of huge databases that contain thousands of music titles. In this study, our interest is about music genre organization. We propose a learning and classification methodology for automatic genre classification able to group several music samples based on their characteristics (this is achieved by the proposed learning process) as well as classify a new test music into the previously learned created groups(this is achieved by the proposed classification process). The learning method intends to group the music samples into di erent clusters only based on audio features and without any previous knowledge on the genre of the samples, and therefore it follows an unsupervised methodology. In addition a Model-Based approach is followed to generate clusters as we do not provide any information about the number of genres in the dataset. Features are related with rhythm analysis, timbre, melody, among others. In addition, Mahalanobis distance was used so that the classification method can deal with non-spherical clusters. The proposed learning method achieves a clustering accuracy of 55% when the dataset contains 11 di erent music genres: Blues, Classical, Country, Disco, Fado, Hiphop, Jazz, Metal,Pop, Reggae and Rock. The clustering accuracy improves significantly when the number of genres is reduced; with 4 genres (Classical, Fado, Metal and Reggae), we obtain an accuracy of 100%. As for the classification process, 82% of the submitted music samples were correctly classified

A flexible bio-inspired hierarchical model for analyzing musical timbre

A flexible and multipurpose bio-inspired hierarchical model for analyzing musical timbre is presented in this paper. Inspired by findings in the fields of neuroscience, computational neuroscience, and psychoacoustics, not only does the model extract spectral and temporal characteristics of a signal, but it also analyzes amplitude modulations on different timescales. It uses a cochlear filter bank to resolve the spectral components of a sound, lateral inhibition to enhance spectral resolution, and a modulation filter bank to extract the global temporal envelope and roughness of the sound from amplitude modulations. The model was evaluated in three applications. First, it was used to simulate subjective data from two roughness experiments. Second, it was used for musical instrument classification using the k-NN algorithm and a Bayesian network. Third, it was applied to find the features that characterize sounds whose timbres were labeled in an audiovisual experiment. The successful application of the proposed model in these diverse tasks revealed its potential in capturing timbral information

Automatic musical instrument recognition for multimedia indexing

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia InformáticaThe subject of automatic indexing of multimedia has been a target of numerous discussion and study. This interest is due to the exponential growth of multimedia content and the subsequent need to create methods that automatically catalogue this data. To fulfil this idea, several projects and areas of study have emerged. The most relevant of these are the MPEG-7 standard, which defines a standardized system for the representation and automatic extraction of information present in the content, and Music Information Retrieval (MIR), which gathers several paradigms and areas of study relating to music. The main approach to this indexing problem relies on analysing data to obtain and identify descriptors that can help define what we intend to recognize (as, for instance,musical instruments, voice, facial expressions, and so on), this then provides us with information we can use to index the data. This dissertation will focus on audio indexing in music, specifically regarding the recognition of musical instruments from recorded musical notes. Moreover, the developed system and techniques will also be tested for the recognition of ambient sounds (such as the sound of running water, cars driving by, and so on). Our approach will use non-negative matrix factorization to extract features from various types of sounds, these will then be used to train a classification algorithm that will be then capable of identifying new sounds

Combining timbric and rhythmic features for semantic music tagging

In this thesis we propose a novel approach to semantic music tagging. The project uses a modified Hidden Markov Model to semantically link two acoustic features. We make the assumption that acoustically similar songs have similar tags. We model our known collection as a graph where the states represent the songs and the model's probabilities are related\nto the timbric and rhythmic similarity. Tags are inferred from songs in acoustically meaningful paths, all starting from the query song

Music similarity analysis using the big data framework spark

A parameterizable recommender system based on the Big Data processing framework Spark is introduced, which takes multiple tonal properties of music into account and is capable of recommending music based on a user's personal preferences. The implemented system is fully scalable; more songs can be added to the dataset, the cluster size can be increased, and the possibility to add different kinds of audio features and more state-of-the-art similarity measurements is given. This thesis also deals with the extraction of the required audio features in parallel on a computer cluster. The extracted features are then processed by the Spark based recommender system, and song recommendations for a dataset consisting of approximately 114000 songs are retrieved in less than 12 seconds on a 16 node Spark cluster, combining eight different audio feature types and similarity measurements.Ein parametrisierbares Empfehlungssystem, basierend auf dem Big Data Framework Spark, wird präsentiert. Dieses berücksichtigt verschiedene klangliche Eigenschaften der Musik und erstellt Musikempfehlungen basierend auf den persönlichen Vorlieben eines Nutzers. Das implementierte Empfehlungssystem ist voll skalierbar. Mehr Lieder können dem Datensatz hinzugefügt werden, mehr Rechner können in das Computercluster eingebunden werden und die Möglichkeit andere Audiofeatures und aktuellere Ähnlichkeitsmaße hizuzufügen und zu verwenden, ist ebenfalls gegeben. Des Weiteren behandelt die Arbeit die parallele Berechnung der benötigten Audiofeatures auf einem Computercluster. Die Features werden von dem auf Spark basierenden Empfehlungssystem verarbeitet und Empfehlungen für einen Datensatz bestehend aus ca. 114000 Liedern können unter Berücksichtigung von acht verschiedenen Arten von Audiofeatures und Abstandsmaßen innerhalb von zwölf Sekunden auf einem Computercluster mit 16 Knoten berechnet werden