Classification of Dance Music by Periodicity Patterns

This paper addresses the genre classification problem for a specific subset of music, standard and Latin ballroom dance music, using a classification method based only on timing information. We compare two methods of extracting periodicities from audio recordings in order to find the metrical hierarchy and timing patterns by which the style of the music can be recognised: the first method performs onset detection and clustering of inter-onset intervals; the second uses autocorrelation on the amplitude envelopes of band-limited versions of the signal as its method of periodicity detection. The relationships between periodicities are then used to find the metrical hierarchy and to estimate the tempo at the beat and measure levels of the hierarchy. The periodicities are then interpreted as musical note values, and the estimated tempo, meter and the distribution of periodicities are used to predict the style of music using a simple set of rules. The methods are evaluated with a test set of standard and Latin dance music, for which the style and tempo are given on the CD cover, providing a "ground truth" by which the automatic classification can be measured

Playsound.space: enhancing a live music performance tool with semantic recommendations

Playsound is an experimental client for the Freesound API. Its main aim is to provide a simple and intuitive tool for the collaborative composition based on Freesound samples. In this paper, an approach based on Semantic Web technologies to provide recommendations to Playsound users is presented. A Semantic Web of Things architecture is proposed, showing loosely coupled, independent software agents interoperating by means of a semantic publish/subscribe platform and a set of ontologies to describe agents, audio contents, input/output of audio analytics tools and recommendations. Preliminary tests show that the designed architecture adapts well to environments where services can be discovered and seamlessly orchestrated on the fly, resulting in a dynamic workflow

Three Dimensional Continuous DP Algorithm for Multiple Pitch Candidates in Music Information Retrieval System

This paper threats theoretical and practical issues that implement a music information retrieval system based on query by humming. In order to extract accuracy features from the user's humming, we propose a new retrieval method based on multiple pitch candidates. Extracted multiple pitches have shown to be very important parameters in determining melodic similarity, but it is also clear that the confidence measures feature which are obtained from the power are important as well. Furthermore, we propose extending the traditional DP algorithm to three dimensions so that multiple pitch candidates can be treated. Simultaneously, at the melody representation technique, we propose the DP paths are changed dynamically to be able to take relative values so that they can respond to insert or omit notes

Conventional and periodic N-grams in the transcription of drum sequences

In this paper, we describe a system for transcribing polyphonic drum sequences from an acoustic signal to a symbolic representation. Low-level signal analysis is done with an acoustic model consisting of a Gaussian mixture model and a support vector machine. For higher-level modeling, periodic N-grams are proposed to construct a "language model" for music, based on the repetitive nature of musical structure. Also, a technique for estimating relatively long N-grams is introduced. The performance of N-grams in the transcription was evaluated using a database of realistic drum sequences from different genres and yielded a performance increase of 7.6 % compared to a the use of only prior (unigram) probabilities with the acoustic model

Exploring Music Collections by Browsing Different Views

The availability of large music collections calls for ways to efficiently access and explore them. We present a new approach which uses descriptors derived from audio analysis and meta-information to create different views of a collection. Such views can have a focus on timbre, rhythm, artist, style or other aspects of music. For each view the pieces of music are organized on a map in such a way that similar pieces are located close to each other. The maps are visualized using an Islands of Music metaphor where islands represent groups of similar pieces. The different maps are linked to each other using a new technique to align Self-Organizing Maps. The user is able to browse the collection and explore different aspects by gradually changing focus from one view to another. We demonstrate our approach on a small collection using a user defined view and two views generated from audio analysis, namely, beat periodicity as an aspect of rhythm and spectral information as an aspect of timbre

Feature Extraction for Music Information Retrieval

Copyright c © 2009 Jesper Højvang Jensen, except where otherwise stated