Folk-dance airs /

"The dance-airs in this collection were noted down in Oxfordshire, Gloucestershire, Devonshire, Somerset, and Derbyshire."

Qin pu xie sheng : 6 juan ; Shi si jun sheng lü biao dong xiao pu he kan /

Reprint of Ting zhen xuan kan ben.

Altnorwegische Romanze mit Variationen. Op. 51.

For orchestra.

Nuit sur le mont Triglav. Troisième acte de l'opéra-ballet Mlada.

Mode of access: Internet.

Seasonal festivals and pageants : spring pantomime /

Music arranged for pianoforte.

Musikalische Werke.

"Thematisches Verzeichniss der Flötensonaten": v. 1, p. xix-xxii.

Sang und Klang im XIX/XX Jahrhundert. Ernstes und Heiteres aus dem Reiche der Töne : mit einer Anzahl Biographien und Portraits.

Volume designation from foreword.

Berceuse héroïque.

For orchestra; originally for piano.

24 [i.e. Vierundzwanzig] ukrainische Volksweisen : für Klavier zu zwei Händen.

Title, captions, etc. also in Russian.

Alla sarabanda : from Phantasy quintet for strings ; Antiphon : from Five mystical songs /

Publisher no.: 1.5076.

Rêverie /

Caption title.

Album of pianoforte works.

Der Bauer eine Schelm, overture, op. 37. - Waltzes from op. 54. - Legends from op. 59. - Slavonic dances from op. 46 and 72.

Design de comunicação aplicado à educação musical

Dissertação de Mestrado para obtenção do grau de Mestre em Design de Comunicação, apresentada na Universidade de Lisboa - Faculdade de Arquitectura.

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.

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.