395 research outputs found
Drum Transcription via Classification of Bar-level Rhythmic Patterns
acceptedMatthias Mauch is supported by a Royal Academy of Engineering
Research Fellowshi
Real-Time Audio-to-Score Alignment of Music Performances Containing Errors and Arbitrary Repeats and Skips
This paper discusses real-time alignment of audio signals of music
performance to the corresponding score (a.k.a. score following) which can
handle tempo changes, errors and arbitrary repeats and/or skips (repeats/skips)
in performances. This type of score following is particularly useful in
automatic accompaniment for practices and rehearsals, where errors and
repeats/skips are often made. Simple extensions of the algorithms previously
proposed in the literature are not applicable in these situations for scores of
practical length due to the problem of large computational complexity. To cope
with this problem, we present two hidden Markov models of monophonic
performance with errors and arbitrary repeats/skips, and derive efficient
score-following algorithms with an assumption that the prior probability
distributions of score positions before and after repeats/skips are independent
from each other. We confirmed real-time operation of the algorithms with music
scores of practical length (around 10000 notes) on a modern laptop and their
tracking ability to the input performance within 0.7 s on average after
repeats/skips in clarinet performance data. Further improvements and extension
for polyphonic signals are also discussed.Comment: 12 pages, 8 figures, version accepted in IEEE/ACM Transactions on
Audio, Speech, and Language Processin
Recommended from our members
Automatic transcription of pitched and unpitched sounds from polyphonic music
Automatic transcription of polyphonic music has been an active research field for several years and is considered by many to be a key enabling technology in music signal processing. However, current transcription approaches either focus on detecting pitched sounds (from pitched musical instruments) or on detecting unpitched sounds (from drum kits). In this paper, we propose a method that jointly transcribes pitched and unpitched sounds from polyphonic music recordings. The proposed model extends the probabilistic latent component analysis algorithm and supports the detection of pitched sounds from multiple instruments as well as the detection of unpitched sounds from drum kit components, including bass drums, snare drums, cymbals, hi-hats, and toms. Our experiments based on polyphonic Western music containing both pitched and unpitched instruments led to very encouraging results in multi-pitch detection and drum transcription tasks
Deep Learning Techniques for Music Generation -- A Survey
This paper is a survey and an analysis of different ways of using deep
learning (deep artificial neural networks) to generate musical content. We
propose a methodology based on five dimensions for our analysis:
Objective - What musical content is to be generated? Examples are: melody,
polyphony, accompaniment or counterpoint. - For what destination and for what
use? To be performed by a human(s) (in the case of a musical score), or by a
machine (in the case of an audio file).
Representation - What are the concepts to be manipulated? Examples are:
waveform, spectrogram, note, chord, meter and beat. - What format is to be
used? Examples are: MIDI, piano roll or text. - How will the representation be
encoded? Examples are: scalar, one-hot or many-hot.
Architecture - What type(s) of deep neural network is (are) to be used?
Examples are: feedforward network, recurrent network, autoencoder or generative
adversarial networks.
Challenge - What are the limitations and open challenges? Examples are:
variability, interactivity and creativity.
Strategy - How do we model and control the process of generation? Examples
are: single-step feedforward, iterative feedforward, sampling or input
manipulation.
For each dimension, we conduct a comparative analysis of various models and
techniques and we propose some tentative multidimensional typology. This
typology is bottom-up, based on the analysis of many existing deep-learning
based systems for music generation selected from the relevant literature. These
systems are described and are used to exemplify the various choices of
objective, representation, architecture, challenge and strategy. The last
section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P.
Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music
Generation, Computational Synthesis and Creative Systems, Springer, 201
An review of automatic drum transcription
In Western popular music, drums and percussion are an important means to emphasize and shape the rhythm, often deļ¬ning the musical style. If computers were able to analyze the drum part in recorded music, it would enable a variety of rhythm-related music processing tasks. Especially the detection and classiļ¬cation of drum sound events by computational methods is considered to be an important and challenging research problem in the broader ļ¬eld of Music Information Retrieval. Over the last two decades, several authors have attempted to tackle this problem under the umbrella term Automatic Drum Transcription(ADT).This paper presents a comprehensive review of ADT research, including a thorough discussion of the task-speciļ¬c challenges, categorization of existing techniques, and evaluation of several state-of-the-art systems. To provide more insights on the practice of ADT systems, we focus on two families of ADT techniques, namely methods based on Nonnegative Matrix Factorization and Recurrent Neural Networks. We explain the methodsā technical details and drum-speciļ¬c variations and evaluate these approaches on publicly available datasets with a consistent experimental setup. Finally, the open issues and under-explored areas in ADT research are identiļ¬ed and discussed, providing future directions in this ļ¬el
- ā¦