Cognitive Musicology – Praised and Reproved

Cognitive sciences have conquered a vast area of the humanities in the last few decades, some of the are regarded as a special branch of the disciplines concerned, such as cognitive musicology. It is a synthesis of various disciplines. Cognitive methods have done a great deal in the direction of a holistic approach. The neuroscience comes to the aid of musicology, it can do more than suggest a cognitive approach it the usual sense. It paves the way for a holistic method, where music as a whole is related to the body as a whole. The results of scholars from various subject fields lead to researches into the relationships between human and musical gestures, and to human and musical behaviour patterns. Not the least component is the character of sound productions, analysable by sound spectography

Wavelet-filtering of symbolic music representations for folk tune segmentation and classification

The aim of this study is to evaluate a machine-learning method in which symbolic representations of folk songs are segmented and classified into tune families with Haar-wavelet filtering. The method is compared with previously proposed Gestaltbased method. Melodies are represented as discrete symbolic pitch-time signals. We apply the continuous wavelet transform (CWT) with the Haar wavelet at specific scales, obtaining filtered versions of melodies emphasizing their information at particular time-scales. We use the filtered signal for representation and segmentation, using the wavelet coefficients ’ local maxima to indicate local boundaries and classify segments by means of k-nearest neighbours based on standard vector-metrics (Euclidean, cityblock), and compare the results to a Gestalt-based segmentation method and metrics applied directly to the pitch signal. We found that the wavelet based segmentation and waveletfiltering of the pitch signal lead to better classification accuracy in cross-validated evaluation when the time-scale and other parameters are optimized. 1

Techniques for generative melodies inspired by music cognition

This article presents a series of algorithmic techniques for melody generation, inspired by models of music cognition. The techniques are designed for interactive composition, and so privilege brevity, simplicity, and flexibility over fidelity to the underlying models. The cognitive models canvassed span gestalt, preference rule, and statistical learning perspectives; this is a diverse collection with a common thread—the centrality of “expectations” to music cognition. We operationalize some recurrent themes across this collection as probabilistic descriptions of melodic tendency, codifying them as stochastic melody-generation techniques. The techniques are combined into a concise melody generator, with salient parameters exposed for ready manipulation in real time. These techniques may be especially relevant to algorithmic composers, the live-coding community, and to music psychologists and theorists interested in how computational interpretations of cognitive models “sound” in practice