Automatic Music Genre Classification of Audio Signals with Machine Learning Approaches

Musical genre classification is put into context byexplaining about the structures in music and how it is analyzedand perceived by humans. The increase of the music databaseson the personal collection and the Internet has brought a greatdemand for music information retrieval, and especiallyautomatic musical genre classification. In this research wefocused on combining information from the audio signal thandifferent sources. This paper presents a comprehensivemachine learning approach to the problem of automaticmusical genre classification using the audio signal. Theproposed approach uses two feature vectors, Support vectormachine classifier with polynomial kernel function andmachine learning algorithms. More specifically, two featuresets for representing frequency domain, temporal domain,cepstral domain and modulation frequency domain audiofeatures are proposed. Using our proposed features SVM act asstrong base learner in AdaBoost, so its performance of theSVM classifier cannot improve using boosting method. Thefinal genre classification is obtained from the set of individualresults according to a weighting combination late fusionmethod and it outperformed the trained fusion method. Musicgenre classification accuracy of 78% and 81% is reported onthe GTZAN dataset over the ten musical genres and theISMIR2004 genre dataset over the six musical genres,respectively. We observed higher classification accuracies withthe ensembles, than with the individual classifiers andimprovements of the performances on the GTZAN andISMIR2004 genre datasets are three percent on average. Thisensemble approach show that it is possible to improve theclassification accuracy by using different types of domainbased audio features

Using EEG-validated Music Emotion Recognition Techniques to Classify Multi-Genre Popular Music for Therapeutic Purposes

Music is observed to possess significant beneficial effects to human mental health, especially for patients undergoing therapy and older adults. Prior research focusing on machine recognition of the emotion music induces by classifying low-level music features has utilized subjective annotation to label data for classification. We validate this approach by using an electroencephalography-based approach to cross-check the predictions of music emotion made with the predictions from low-level music feature data as well as collected subjective annotation data. Collecting 8-channel EEG data from 10 participants listening to segments of 40 songs from 5 different genres, we obtain a subject-independent classification accuracy for EEG test data of 98.2298% using an ensemble classifier. We also classify low-level music features to cross-check music emotion predictions from music features with the predictions from EEG data, obtaining a classification accuracy of 94.9774% using an ensemble classifier. We establish links between specific genre preference and perceived valence, validating individualized approaches towards music therapy. We then use the classification predictions from the EEG data and combine it with the predictions from music feature data and subjective annotations, showing the similarity of the predictions made by these approaches, validating an integrated approach with music features and subjective annotation to classify music emotion. We use the music feature-based approach to classify 250 popular songs from 5 genres and create a musical playlist application to create playlists based on existing psychological theory to contribute emotional benefit to individuals, validating our playlist methodology as an effective method to induce positive emotional response

A mood-based music classification and exploration system

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (p. 89-93).Mood classification of music is an emerging domain of music information retrieval. In the approach presented here features extracted from an audio file are used in combination with the affective value of song lyrics to map a song onto a psychologically based emotion space. The motivation behind this system is the lack of intuitive and contextually aware playlist generation tools available to music listeners. The need for such tools is made obvious by the fact that digital music libraries are constantly expanding, thus making it increasingly difficult to recall a particular song in the library or to create a playlist for a specific event. By combining audio content information with context-aware data, such as song lyrics, this system allows the listener to automatically generate a playlist to suit their current activity or mood.by Owen Craigie Meyers.S.M

Proceedings of the 6th International Workshop on Folk Music Analysis, 15-17 June, 2016

The Folk Music Analysis Workshop brings together computational music analysis and ethnomusicology. Both symbolic and audio representations of music are considered, with a broad range of scientific approaches being applied (signal processing, graph theory, deep learning). The workshop features a range of interesting talks from international researchers in areas such as Indian classical music, Iranian singing, Ottoman-Turkish Makam music scores, Flamenco singing, Irish traditional music, Georgian traditional music and Dutch folk songs. Invited guest speakers were Anja Volk, Utrecht University and Peter Browne, Technological University Dublin

An Analytical Methodology for the Investigation of the Relationship of Music and Lyrics in Popular Music

This thesis details the conception and design of a new methodology for examining pop songs holistically; considering both music and lyrics and examining the synergies between the two. Central to this methodology is the application of a data extraction framework, which has been designed to mine information about musical and lyrical phenomena. This framework operates as a common source for producing data about two very different media, avoiding individual interpretation where this is possible. The methodology has been designed to address specific questions about the relationship between music and lyrics, but the main purpose of the thesis is to evaluate the usefulness of the endeavour. In order to examine the efficacy of this approach, the framework was used to populate a dataset made up of a sample of 300 songs, which was subsequently explored and analysed through a series of case studies which investigate combinations of metrics concerned with music and lyrics for the whole sample, as well as analysis of specific subsets defined by a range of parameters. These case studies have demonstrated the various ways this approach might be used, as well as working as proof of concept. The conclusion of the thesis reviews the various case studies in the context of presenting potential uses of the framework as a tool and the broader methodology by other scholars. There is also a consideration of how the overall data might be affected by the inclusion of genres and styles that are not included in the initial sample set

Content-Based Music Recommendation using Deep Learning

Music streaming services use recommendation systems to improve the customer experience by generating favorable playlists and by fostering the discovery of new music. State of the art recommendation systems use both collaborative filtering and content-based recommendation methods. Collaborative filtering suffers from the cold start problem; it can only make recommendations for music for which it has enough user data, so content-based methods are preferred. Most current content-based recommendation systems use convolutional neural networks on the spectrograms of track audio. The architectures are commonly borrowed directly from the field of computer vision. It is shown in this study that musically-motivated convolutional neural network architectures outperform architectures that are highly-optimized for image-related tasks. A content-based recommendation model is built using musically-motivated deep learning architectures. The model is shown to be able to map an artist onto an artist embedding space where its nearest neighbors by cosine similarity are related artists and make good recommendations. It is also shown that metadata, such as lyrics, artist origin, and year, significantly improve these mappings when combined with raw audio data

Metal Music in the Federal Republic of Germany in the 1980s and 1990s:Record Production, Industry, and Heritage

This commentary and the associated portfolio of publications examine the two formative decades of metal music in the Federal Republic of Germany in the 1980s and 1990s from a production perspective. A multi-methodical and mixed-data research design is used, combining qualitative interviews and document and music analyses. The scope is narrowed to three areas: 1) the sonic signatures of German metal compared to foreign productions (mainly from the UK and USA); 2) the business and recording infrastructure of German metal production; 3) the heritage and cultural preservation of recorded metal music in Germany. Re 1) The research proposes that German metal has a sonic, yet vaguely defined, signature, given the complexity of such an attribution. Apart from the detailed nuances that distinguish German metal from other parts of the world, further musical factors like the individual styles of bands and genre conventions are shown to play a significant role. Through interviews, it is demonstrated that even producers who have been instrumental in the first three decades of German metal find it challenging to define a German metal signature and differ in their views. As to what influences the perception and discourse of place or culture-specific sounds, they highlight further contextual aspects potentially more significant than auditory perception, such as imagined communities, myths, symbols, and historically developed stereotypes. Re 2) The research examines the production landscape of early (West) German metal music. It proposes that common depictions in journalistic media fall short, limiting German metal history to a few independent record companies (Noise, Steamhammer, Nuclear Blast, Century Media) and producers (e.g., Harris Johns). The publications portfolio confirms a significant contribution of these actors, but for early metal to flourish, the development relied on a more extensive network of record companies, distributors, managers, and recordists. It is shown that German subcultural rock and metal production was historically disadvantaged against the dominant Anglo-American markets. However, as the research demonstrates, the scene’s persistence and community-oriented ethos eventually advanced Germany to a considerable production location for German and foreign metal bands. Re 3) The research reflects on the need to preserve the heritage of metal music as part of German national culture. It examines the tendency that music producers and studio owners do not consider artefacts used or created when producing a record worthy of heritage, believing these mainly serve the nostalgic interests of those involved in the process. According to them, what should be preserved is the released record as the most relevant contribution to metal culture. That is why recordists have taken on audio preservation, which is typically the responsibility of record companies, to prevent cultural artefacts from being lost. The research wishes to draw attention to the risk of losing audio and other material artefacts from the genre’s production, aiming to stimulate reflection and encourage preservative action