Conventional and periodic N-grams in the transcription of drum sequences

In this paper, we describe a system for transcribing polyphonic drum sequences from an acoustic signal to a symbolic representation. Low-level signal analysis is done with an acoustic model consisting of a Gaussian mixture model and a support vector machine. For higher-level modeling, periodic N-grams are proposed to construct a "language model" for music, based on the repetitive nature of musical structure. Also, a technique for estimating relatively long N-grams is introduced. The performance of N-grams in the transcription was evaluated using a database of realistic drum sequences from different genres and yielded a performance increase of 7.6 % compared to a the use of only prior (unigram) probabilities with the acoustic model

A Corpus-based Study Of Rhythm Patterns

We present a corpus-based study of musical rhythm, based on a collection of 4.8 million bar-length drum patterns extracted from 48,176 pieces of symbolic music. Approaches to the analysis of rhythm in music information retrieval to date have focussed on low-level features for retrieval or on the detection of tempo, beats and drums in audio recordings. Musicological approaches are usually concerned with the description or implementation of manmade music theories. In this paper, we present a quantitative bottom-up approach to the study of rhythm that relies upon well-understood statistical methods from natural language processing. We adapt these methods to our corpus of music, based on the realisation that—unlike words—barlength drum patterns can be systematically decomposed into sub-patterns both in time and by instrument. We show that, in some respects, our rhythm corpus behaves like natural language corpora, particularly in the sparsity of vocabulary. The same methods that detect word collocations allow us to quantify and rank idiomatic combinations of drum patterns. In other respects, our corpus has properties absent from language corpora, in particular, the high amount of repetition and strong mutual information rates between drum instruments. Our findings may be of direct interest to musicians and musicologists, and can inform the design of ground truth corpora and computational models of musical rhythm. 1

Drum Transcription via Classification of Bar-level Rhythmic Patterns

acceptedMatthias Mauch is supported by a Royal Academy of Engineering Research Fellowshi

Automatic Drum Transcription and Source Separation

While research has been carried out on automated polyphonic music transcription, to-date the problem of automated polyphonic percussion transcription has not received the same degree of attention. A related problem is that of sound source separation, which attempts to separate a mixture signal into its constituent sources. This thesis focuses on the task of polyphonic percussion transcription and sound source separation of a limited set of drum instruments, namely the drums found in the standard rock/pop drum kit. As there was little previous research on polyphonic percussion transcription a broad review of music information retrieval methods, including previous polyphonic percussion systems, was also carried out to determine if there were any methods which were of potential use in the area of polyphonic drum transcription. Following on from this a review was conducted of general source separation and redundancy reduction techniques, such as Independent Component Analysis and Independent Subspace Analysis, as these techniques have shown potential in separating mixtures of sources. Upon completion of the review it was decided that a combination of the blind separation approach, Independent Subspace Analysis (ISA), with the use of prior knowledge as used in music information retrieval methods, was the best approach to tackling the problem of polyphonic percussion transcription as well as that of sound source separation. A number of new algorithms which combine the use of prior knowledge with the source separation abilities of techniques such as ISA are presented. These include sub-band ISA, Prior Subspace Analysis (PSA), and an automatic modelling and grouping technique which is used in conjunction with PSA to perform polyphonic percussion transcription. These approaches are demonstrated to be effective in the task of polyphonic percussion transcription, and PSA is also demonstrated to be capable of transcribing drums in the presence of pitched instruments

Unpitched percussion transcription in audio signals

Die Forschung im Bereich der inhaltsbasierten Beschreibung von Musik hat an Bedeutung gewonnen, seitdem die Menge digital erhältlicher Musik unüberschaubar geworden ist. Eines der interessantesten und schwierigsten Probleme, unter der Vielzahl von Disziplinen in diesem Feld, ist das der Musiktranskription. Dieser Begriff bezeichnet im weitesten Sinn die zeitliche Erkennung musikalischer Ereignisse und die Benennung der daran beteiligten Instrumente. Bisherige Forschung in diesem Bereich konzentrierte sich hauptschlich auf die Extraktion von melodischen und tonalen Informationen, bis vor kurzem der Extraktion von rhythmischen Strukturen derselbe Stellenwert beigemessen wurde. Da Schlaginstrumente das rhythmische Rückgrat eines musikalischen Stückes bilden, spielt deren Transkription eine entscheidende Rolle bei der Darstellung und dem Verständnis von Musik. Diese Masterarbeit beschreibt angewendete Signalverarbeitungstechniken im Bereich der Transkription von Schlaginstrumenten und stellt ein Vorlagen-basiertes Verfahren im Detail vor, das im Verlauf dieser Masterarbeit implementiert und getestet worden ist.Content-based description of music has become a significant research topic, since technological advances let the amount of digitally available music explode. One of the most interesting and challenging problems, among the wide range of disciplines in this field, is that of music transcription. The term transcription refers to the task of estimating the temporal locations of sound events and recognising the instruments which have been used to produce them. Research in this discipline primarily focused on the extraction of melodic and tonal information, until more recently the extraction of rhythmic structures received the same degree of attention. As percussive instruments form the rhythmic backbone of a musical piece, their transcription is a key component in representing and understanding music. This thesis explores state of the art signal processing techniques that have found application in percussion transcription and describes a template-matching-based transcription system in more detail, which has been implemented and evaluated in the course of this thesis

Auditing & EDP

https://egrove.olemiss.edu/aicpa_guides/1018/thumbnail.jp