An approach to melodic segmentation and classification based on filtering with the Haar wavelet

We present a novel method of classification and segmentation of melodies in symbolic representation. The method is based on filtering pitch as a signal over time with the Haar-wavelet, and we evaluate it on two tasks. The filtered signal corresponds to a single-scale signal ws from the continuous Haar wavelet transform. The melodies are first segmented using local maxima or zero-crossings of ws. The segments of ws are then classified using the k–nearest neighbour algorithm with Euclidian and city-block distances. The method proves more effective than using unfiltered pitch signals and Gestalt-based segmentation when used to recognize the parent works of segments from Bach’s Two-Part Inventions (BWV 772–786). When used to classify 360 Dutch folk tunes into 26 tune families, the performance of the method is comparable to the use of pitch signals, but not as good as that of string-matching methods based on multiple features

Compression-based Modelling of Musical Similarity Perception

Similarity is an important concept in music cognition research since the similarity between (parts of) musical pieces determines perception of stylistic categories and structural relationships between parts of musical works. The purpose of the present research is to develop and test models of musical similarity perception inspired by a transformational approach which conceives of similarity between two perceptual objects in terms of the complexity of the cognitive operations required to transform the representation of the first object into that of the second, a process which has been formulated in informationtheoretic terms. Specifically, computational simulations are developed based on compression distance in which a probabilistic model is trained on one piece of music and then used to predict, or compress, the notes in a second piece. The more predictable the second piece according to the model, the more efficiently it can be encoded and the greater the similarity between the two pieces. The present research extends an existing information-theoretic model of auditory expectation (IDyOM) to compute compression distances varying in symmetry and normalisation using high-level symbolic features representing aspects of pitch and rhythmic structure. Comparing these compression distances with listeners’ similarity ratings between pairs of melodies collected in three experiments demonstrates that the compression-based model provides a good fit to the data and allows the identification of representations, model parameters and compression-based metrics that best account for musical similarity perception. The compression-based model also shows comparable performance to the best-performing algorithms on the MIREX 2005 melodic similarity task

Melodic String Matching via Interval Consolidation and Fragmentation

Part 7: First Mining Humanistic Data Workshop (MHDW 2012)International audienceIn this paper, we address the problem of melodic string matching that enables identification of varied (ornamented) instances of a given melodic pattern. To this aim, a new set of edit distance operations adequate for pitch interval strings is introduced. Insertion, deletion and replacement operations are abolished as irrelevant. Consolidation and fragmentation are retained, but adapted to the pitch interval domain, i.e., two or more intervals of one string may be matched to an interval from a second string through consolidation or fragmentation. The melodic interval string matching problem consists of finding all occurrences of a given pattern in a melodic sequence that takes into account exact matches, consolidations and fragmentations of intervals in both the sequence and the pattern. We show some properties of the problem and an algorithm that solves this problem is proposed

Tempo-Express, a CBR Approach to Musical Tempo Transformations

The original publication is available at www.springerlink.comIn this paper, we describe a CBR system for applying musically acceptable tempo transformations to monophonic audio recordings of musical performances. Within the tempo transformation process, the expressivity of the performance is adjusted in such a way that the result sounds natural for the new tempo. A case base of previously performed melodies is used to infer the appropriate expressivity. Tempo transformation is one of the audio post-processing tasks manually done in audiolabs. Automatizing this process may, therefore, be of industrial interest.This research has been partially supported by the Spanish Ministry of Science and Technology under the project TIC 2003-07776-C2-02 "CBR-ProMusic: Content-based Music Processing using CBR" and EU-FEDER funds.Peer reviewe