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.

Rights of Portugal, in reference to Great Britain and the question of the slave trade, or, the manifesto and protest of the weak, against the ingratitude, oppression, and violence of the strong ...

Enquanto os políticos e diplomatas polemizavam e agiam, Thomas Buxton publicava, depois de viajar ao Brasil e ao Caribe, o mais completo levantamento sobre o tráfico negreiro jamais feito até então.

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.

Differential cognitive responses to guqin music and piano music in Chinese subjects: an event-related potential study

目的 研究古琴(一种古老的中国乐器)和钢琴音乐对认知的影响.方法 记录和分析了中国被试在两种音乐背景(古琴音乐,钢琴音乐)下完成听觉oddball任务的行为和事件相关电位(event-related potential,ERP)数据.结果 中国被试在本土文化的音乐环境(古琴音乐)下,前额区诱导出更大的P300,这一结果和已有的相关研究是相符的.同时,不同音乐背景对ERP产生的影响在N1和LPC(包括P300和P500)上也表现出差别:中国被试在古琴音乐背景下比钢琴音乐背景下表现出更多的右前侧颞叶的参与.结论 因为古琴音乐的五声调式和汉语发音的音调具有对应关系,因此我们推断在古琴音乐下所表现出的这种特性与被试的汉语环境有关.

Crossmodal effects of Guqin and piano music on selective attention: An event-related potential study

To compare the effects of music from different cultural environments (Guqin: Chinese music; piano: Western music) on crossmodal selective attention, behavioral and event-related potential (ERP) data in a standard two-stimulus visual oddball task were reco

Crossmodal effects of Guqin and piano music on selective attention: An event-related potential study (vol 466, pg 21, 2009)

Special thanks to Christopher Blair and Mumtaz Baig for their suggestions. This work was supported by National Basic Research Program of China (973 Program, 2007CB411600), National Natural Science Foundation of China (30621092), and Bureau of Science and Technology of Yunnan Province.