Blind Clustering of Popular Music Recordings Based on Singer Voice Characteristics

This paper presents an effective technique for automatically clustering undocumented music recordings based on their associated singer. This serves as an indispensable step towards indexing and content-based information retrieval of music by singer. The proposed clustering system operates in an unsupervised manner, in which no prior information is available regarding the characteristics of singer voices, nor the population of singers. Methods are presented to separate vocal from non-vocal regions, to isolate the singers' vocal characteristics from the background music, to compare the similarity between singers' voices, and to determine the total number of unique singers from a collection of songs. Experimental evaluations conducted on a 200-track pop music database confirm the validity of the proposed system

STILL RECORDING AFRICAN MUSIC IN THE FIELD

Field sound recordings are an indispensable source of data for ethnomusicologists. However, to my knowledge there are no standards or guidelines of how this data should be captured and managed. With the progress made in machine learning, it has become vital to record data in a way that also supports the retrieval of information about the music. This article describes a model developed for field recordings that aims to aid an objective data gathering process. This model, developed through an action research process that spanned multiple field recording sessions from 2009–2015, include recording equipment, production processes, the gathering of metadata as well as intellectual property rights. The core principles identified in this research are that field recording systems should be designed to provide accurate feedback as a means of quality control and should capture and manage metadata without relying on secondary tools. The major findings are presented in the form of a checklist that can serve as a point of departure for ethnomusicologists making field recordings

FEATURES FOR MELODY SPOTTING USING HIDDEN MARKOV MODELS

The amount of digitized music stored on personal computers and available on the Internet is growing at a rapid rate. To address the access problem that this creates, we explore adapting HMMbased wordspotting techniques from speech recognition to create a system for melody-based retrieval of songs from a database of digitized music stored in a musically-unstructured format. In this paper, we present the construction of this melody spotter and evaluate its performance when trained under different feature vectors including a musical scale-based subset of the FFT and two Melscale based features. The results show the success of this system under the scale-based features when presented with both perfect melody queries and queries perturbed by minor errors. 1