826 resultados para Organ music
Resumo:
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.
Resumo:
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.
Resumo:
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.
Resumo:
Campylobacter jejuni is an important food-borne pathogen. However, relatively little is understood regarding its pathogenesis, and research is hampered by the lack of a suitable model. Recently, a number of groups have developed assays to study the pathogenic mechanisms of C. jejuni using cell culture models. Here, we report the development of an ex vivo organ culture model, allowing for the maintenance of intestinal mucosal tissue, to permit more complex host-bacterium interactions to be studied. Ex vivo organ culture highlights the propensity for C. jejuni to adhere to mucosal tissue via the flagellum, either as discrete colonies or as multicellular units.
Resumo:
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.
Resumo:
Samples of C. gariepinus collected from the wild and cultured populations in Plateau and Niger States of Nigeria were analyzed for length-weight relationship and organ indices (Gonadosomatic index (GSI), hepatosomatic index (HSI), renalsomatic index (RSI) and somatic fat deposit index (PDI). High correlation and linear relationship between body length and body weight was observed in all sample population (P<0.05). A significant difference was observed between the GSI of males and females of both wild and cultured population and also between females of the wild and cultured population,(P < 0.05).There was no significant difference in HSI, CSI RSI and PDI of all the sample populations (P < 0.05).The importance of length-weight relationship and organ indices in fish production are discussed
Resumo:
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.
Resumo:
Shrimp disease of viral origin have caused large production losses worldwide. This paper presents a case study of shrimp (Penaeus monodon; Penaeus indicus) epizootic disease, covering an area of 1,050 ha in Andhra Pradesh, India. The disease struck shrimp farms in the area in July 1994. Samples from 26 shrimp farms were studied in the laboratory, and the pattern of the disease and of mortality recorded. The disease was classified as infectious hepatopancreatic and lymphoid organ necrosis disease (IHLN).