Compressive sensing for music signals: comparison of transforms with coherent dictionaries

Compressive Sensing (CS) is a new sensing paradigm which permits sampling of a signal at its intrinsic information rate which could be much lower than Nyquist rate, while guaranteeing good quality reconstruction for signals sparse in a linear transform domain. We explore the application of CS formulation to music signals. Since music signals comprise of both tonal and transient nature, we examine several transforms such as discrete cosine transform (DCT), discrete wavelet transform (DWT), Fourier basis and also non-orthogonal warped transforms to explore the effectiveness of CS theory and the reconstruction algorithms. We show that for a given sparsity level, DCT, overcomplete, and warped Fourier dictionaries result in better reconstruction, and warped Fourier dictionary gives perceptually better reconstruction. “MUSHRA” test results show that a moderate quality reconstruction is possible with about half the Nyquist sampling.

Music and symbolic dynamics: the science behind an art

Music signals comprise of atomic notes drawn from a musical scale. The creation of musical sequences often involves splicing the notes in a constrained way resulting in aesthetically appealing patterns. We develop an approach for music signal representation based on symbolic dynamics by translating the lexicographic rules over a musical scale to constraints on a Markov chain. This source representation is useful for machine based music synthesis, in a way, similar to a musician producing original music. In order to mathematically quantify user listening experience, we study the correlation between the max-entropic rate of a musical scale and the subjective aesthetic component. We present our analysis with examples from the south Indian classical music system.

STUDENT'S-t MIXTURE MODEL BASED MULTI-INSTRUMENT RECOGNITION IN POLYPHONIC MUSIC

We address the problem of multi-instrument recognition in polyphonic music signals. Individual instruments are modeled within a stochastic framework using Student's-t Mixture Models (tMMs). We impose a mixture of these instrument models on the polyphonic signal model. No a priori knowledge is assumed about the number of instruments in the polyphony. The mixture weights are estimated in a latent variable framework from the polyphonic data using an Expectation Maximization (EM) algorithm, derived for the proposed approach. The weights are shown to indicate instrument activity. The output of the algorithm is an Instrument Activity Graph (IAG), using which, it is possible to find out the instruments that are active at a given time. An average F-ratio of 0 : 7 5 is obtained for polyphonies containing 2-5 instruments, on a experimental test set of 8 instruments: clarinet, flute, guitar, harp, mandolin, piano, trombone and violin.

Automatic Tonic Identification in Indian Art Music: Approaches and Evaluation

The tonic is a fundamental concept in Indian art music. It is the base pitch, which an artist chooses in order to construct the melodies during a rg(a) rendition, and all accompanying instruments are tuned using the tonic pitch. Consequently, tonic identification is a fundamental task for most computational analyses of Indian art music, such as intonation analysis, melodic motif analysis and rg recognition. In this paper we review existing approaches for tonic identification in Indian art music and evaluate them on six diverse datasets for a thorough comparison and analysis. We study the performance of each method in different contexts such as the presence/absence of additional metadata, the quality of audio data, the duration of audio data, music tradition (Hindustani/Carnatic) and the gender of the singer (male/female). We show that the approaches that combine multi-pitch analysis with machine learning provide the best performance in most cases (90% identification accuracy on average), and are robust across the aforementioned contexts compared to the approaches based on expert knowledge. In addition, we also show that the performance of the latter can be improved when additional metadata is available to further constrain the problem. Finally, we present a detailed error analysis of each method, providing further insights into the advantages and limitations of the methods.

MULTI-INSTRUMENT DETECTION IN POLYPHONIC MUSIC USING GAUSSIAN MIXTURE BASED FACTORIAL HMM

We formulate the problem of detecting the constituent instruments in a polyphonic music piece as a joint decoding problem. From monophonic data, parametric Gaussian Mixture Hidden Markov Models (GM-HMM) are obtained for each instrument. We propose a method to use the above models in a factorial framework, termed as Factorial GM-HMM (F-GM-HMM). The states are jointly inferred to explain the evolution of each instrument in the mixture observation sequence. The dependencies are decoupled using variational inference technique. We show that the joint time evolution of all instruments' states can be captured using F-GM-HMM. We compare performance of proposed method with that of Student's-t mixture model (tMM) and GM-HMM in an existing latent variable framework. Experiments on two to five polyphony with 8 instrument models trained on the RWC dataset, tested on RWC and TRIOS datasets show that F-GM-HMM gives an advantage over the other considered models in segments containing co-occurring instruments.

Music-Theoretic Estimation of Chords and Keys from Audio

This paper proposes a new method for local key and chord estimation from audio signals. This method relies primarily on principles from music theory, and does not require any training on a corpus of labelled audio files. A harmonic content of the musical piece is first extracted by computing a set of chroma vectors. A set of chord/key pairs is selected for every frame by correlation with fixed chord and key templates. An acyclic harmonic graph is constructed with these pairs as vertices, using a musical distance to weigh its edges. Finally, the sequences of chords and keys are obtained by finding the best path in the graph using dynamic programming. The proposed method allows a mutual chord and key estimation. It is evaluated on a corpus composed of Beatles songs for both the local key estimation and chord recognition tasks, as well as a larger corpus composed of songs taken from the Billboard dataset.

Recebido e ofertado: a natureza dos hinos na religião do Santo Daime

A proposta do trabalho é investigar a música e sua centralidade nos rituais do Santo Daime ligados ao Centro Eclético da Fluente Luz Universal Raimundo Irineu Serra (CEFLURIS), fundado na década de 1970. A metodologia utilizada é a observação participante nos trabalhos de hinário, presenciados nas igrejas cariocas Céu do Mar e Jardim Praia da Beira-Mar e do conjunto de dez entrevistas em profundidade. Além de um capítulo da descrição do campo e outro com a revisão bibliográfica da literatura antropológica do Santo Daime, a dissertação conta com três capítulos de análise. O primeiro deles é aberto com a revisão de estudos da etnomusicologia para então abordar o ritual de hinário e a relação entre tempo e música na orientação de tarefas específicas durante as cerimônias; o que constrói entre outras coisas, uma nova concepção de realidade devido aos aspectos poético-musicais aliados ao contexto psicotrópico e ritualístico como um todo. No capítulo seguinte se discute a interpretação nativa que descreve a gênese dos hinos religiosos como um recebimento e suas especificidades, contrapostas a uma idéia de composição musical. As falas do grupo sobre a natureza dos pensamentos, sentimentos e a subjetividade, refletem-se nas noções de sagrado e nas diferenciações hierárquicas nos salões das igrejas daimistas, utilizando o conceito de micropolítica dos sentimentos o dialogo é construído desta vez com os estudos da antropologia das emoções. O terceiro capítulo da análise está voltado para uma discussão sobre as situações que envolvem a oferta de hinos em um complexo circuito de dádivas, quando o daimista presenteia outro membro do grupo por intermédio de canções religiosas, ligando o mundo dos espíritos e os homens pelo ato do presentear. Esta etapa do trabalho também é precedida por uma revisão da literatura antropológica, voltada para estudos clássicos sobre a dádiva, destacando-se a discussão da troca como uma gramática. Todas as fases da análise são introduzidas por revisões teóricas e, da forma como o trabalho está estruturado, os temas abordados nos capítulos iniciais são continuamente retomados ao longo das discussões. O argumento central da dissertação é construído sobre as seguintes questões: Qual o espaço ocupado pela música na vida diária dos seguidores da religião do Santo Daime e dentro dos rituais? De que maneira essas canções são como pontes de ligação entre as esferas sagrada e profana? Como as músicas orientam a práxis religiosa e as relações entre os crentes? De que forma os hinos religiosos do Santo Daime são capazes de suscitar e expressar sentimentos específicos? E enfim: como música e sentimento se articulam na conformação deste tipo de experiência religiosa?

Influence of music on the growth of koi carp, Cyprinus carpio (Pisces: Cyprindae)

An experiment was carried out to investigate the influence of music on the growth of Koi Carp (Cyprinus carpio) by subjecting the fish to music. Weekly growth in weight was recorded and used to calculate the growth rate and specific growth rate. The difference in growth between the control and experiment groups of fishes was statistically tested for significance. It was observed that the growth of fish subjected to music was significantly higher.