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 instr...

In order to represent musical content, pitch and timing information is utilized in the majority of existing work in Symbolic Music Information Retrieval (MIR). Symbolic representations such as MIDI allow the easy calculation of such information and its manipulation. In contrast, most of the existing work in Audio MIR uses timbral and beat information, which can be calculated using automatic com...

Recent work in the sociology of music suggests a declining importance of genre categories. Yet other work in this research stream and in the sociology of classification argues for the continued prevalence of genres as a meaningful tool through which creators, critics and consumers focus their attention in the topology of available works. Building from work in the study of categories and categor...

Automatic classification of music pieces by genre is one of the crucial tasks in music categorization for intelligent navigation. In this work we present a multiExpert genre classification system based on acoustic, musical and timbre features. A novel rhythmic characteristic, 2D beat histogram is used as high-level musical feature. Timbre features are extracted by multiple-f0 detection algorith...

A lot of genre classification work to date has gone into exploring features that demarcate genre either at the lowest (sample) level or the highest level (overall acoustic characteristics of the song). Features evaluated at the sample level are more concerned with sonic qualities like timbre and pitch, whereas higher-level features tend to focus on key, mode, tempo, and other descriptive featur...

We propose new audio features that can be extracted from bass lines. Most previous studies on content-based music information retrieval (MIR) used low-level features such as the mel-frequency cepstral coefficients and spectral centroid. Musical similarity based on these features works well to some extent but has a limit to capture fine musical characteristics. Because bass lines play important ...

We present a system for musical genre classification based on audio features extracted from signals which correspond to distinct musical instrument sources. For the separation of the musical sources, we propose an innovative technique in which the convolutive sparse coding algorithm is applied to several portions of the audio signal. The system is evaluated and its performance is assessed.

We propose a two-step, audio feature-based musical genre classification methodology. First, we identify and separate the various musical instrument sources in the audio signal, using the convolutive sparse coding algorithm. Next, we extract classification features from the separated signals that correspond to distinct musical instrument sources. The methodology is evaluated and its performance ...

To stretch a music piece to a given length is a common demand in people’s daily lives, e.g., in audio-video synchronization and animation production. However, it is not always guaranteed that the stretched music piece is acceptable for general audience since music stretching suffers from people’s perceptual artefacts. Over-stretching a music piece will make it uncomfortable for human psychoacou...

Machine learning techniques for automated musical genre classification are currently widely studied. With large collections of digital musical files, one approach to classification is to classify by musical genres such as pop, rock and classical in Western music. Beat, pitch and temporal related features are extracted from audio signals and various machine learning algorithms are applied for cl...