ACCOUNTING FOR PHASE CANCELLATIONS IN NON-NEGATIVE MATRIX FACTORIZATION USING WEIGHTED DISTANCES

(c)2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Published in: Proc IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), Florence, Italy, 5-9 May 2014

Score-Informed Source Separation for Music Signals

In recent years, the processing of audio recordings by exploiting additional musical knowledge has turned out to be a promising research direction. In particular, additional note information as specified by a musical score or a MIDI file has been employed to support various audio processing tasks such as source separation, audio parameterization, performance analysis, or instrument equalization. In this contribution, we provide an overview of approaches for score-informed source separation and illustrate their potential by discussing innovative applications and interfaces. Additionally, to illustrate some basic principles behind these approaches, we demonstrate how score information can be integrated into the well-known non-negative matrix factorization (NMF) framework. Finally, we compare this approach to advanced methods based on parametric models

Score-Informed Source Separation for Musical Audio Recordings [An overview]

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

The Audio Degradation Toolbox and its Application to Robustness Evaluation

We introduce the Audio Degradation Toolbox (ADT) for the controlled degradation of audio signals, and propose its usage as a means of evaluating and comparing the robustness of audio processing algorithms. Music recordings encountered in practical applications are subject to varied, sometimes unpredictable degradation. For example, audio is degraded by low-quality microphones, noisy recording environments, MP3 compression, dynamic compression in broadcasting or vinyl decay. In spite of this, no standard software for the degradation of audio exists, and music processing methods are usually evaluated against clean data. The ADT fills this gap by providing Matlab scripts that emulate a wide range of degradation types. We describe 14 degradation units, and how they can be chained to create more complex, `real-world' degradations. The ADT also provides functionality to adjust existing ground-truth, correcting for temporal distortions introduced by degradation. Using four different music informatics tasks, we show that performance strongly depends on the combination of method and degradation applied. We demonstrate that specific degradations can reduce or even reverse the performance difference between two competing methods. ADT source code, sounds, impulse responses and definitions are freely available for download

Robust Joint Alignment of Multiple Versions of a Piece of Music

Large music content libraries often comprise multiple versions of a piece of music. To establish a link between different versions, automatic music alignment methods map each position in one version to a corresponding position in another version. Due to the leeway in interpreting a piece, any two versions can differ significantly, for example, in terms of local tempo, articulation, or playing style. For a given pair of versions, these differences can be significant such that even state-of-the-art methods fail to identify a correct alignment. In this paper, we present a novel method that increases the robustness for difficult to align cases. Instead of aligning only pairs of versions as done in previous methods, our method aligns multiple versions in a joint manner. This way, the alignment can be computed by comparing each version not only with one but with several versions, which stabilizes the comparison and leads to an increase in alignment robustness. Using recordings from the Mazurka Project, the alignment error for our proposed method was 14% lower on average compared to a state-of-the-art method, with significantly less outliers (standard deviation 53% lower).Comment: International Society for Music Information Retrieval Conference (ISMIR

Notentext-Informierte Quellentrennung für Musiksignale

codedemo: http://www.audiolabs-erlangen.de/resources/2013-ACMMM-AudioDecomp/codedemo: http://www.audiolabs-erlangen.de/resources/2013-ACMMM-AudioDecomp/codedemo: http://www.audiolabs-erlangen.de/resources/2013-ACMMM-AudioDecomp/codedemo: http://www.audiolabs-erlangen.de/resources/2013-ACMMM-AudioDecomp

Improving Time-Scale Modification of Music Signals Using Harmonic-Percussive Separation

A major problem in time-scale modification (TSM) of music signals is that percussive transients are often perceptually degraded. To prevent this degradation, some TSM approaches try to explicitly identify transients in the input signal and to handle them in a special way. However, such approaches are problematic for two reasons. First, errors in the transient detection have an immediate influence on the final TSM result and, second, a perceptual transparent preservation of transients is by far not a trivial task. In this paper we present a TSM approach that handles transients implicitly by first separating the signal into a harmonic component as well as a percussive component which typically contains the transients. While the harmonic component is modified with a phase vocoder approach using a large frame size, the noise-like percussive component is modified with a simple time-domain overlap-add technique using a short frame size, which preserves the transients to a hig h degree without any explicit transient detection