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 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

Understanding Incubator Farms: Innovative Programs in New Farmer Development

With the average age of farmers climbing close to retirement and an increased demand for locally and sustainably grown food, there is a great need for new and beginning farmers, particularly those engaged in alternative production practices. But the barriers to entry for these new farmers are high; land access, start up costs, finding a spot in the market, and a lack of social support all pose challenges. The last few years have seen an increased interest in supporting beginning farmers and the creation of new programs to do so. Incubator farms are one such promising strategy. Incubator farms are organizations that address the obstacles faced by beginning farmers by offering some combination of farming education, hands-on training, and low-cost access to land and infrastructure to help aspiring farmers start new agricultural businesses. Despite the rise in beginning farmer training programs, Niewolny and Lillard (2010: 65) have described it as one of the most “poorly understood areas of agriculture, food systems, and community development research and practice.” This research aims to further knowledge of this area by studying one approach, incubator farms, in depth and then bridging research and practice. Three case studies of existing incubator farm programs are presented: Viva Farms in Skagit Valley, WA; the Elma C. Lomax Incubator in Cabarrus County, NC; and Urban Edge Farm in Cranston, RI. Research methods include 30 in-person interviews with program staff and new farmer participants, site visits, and a review of program materials. Interview transcripts, field notes, and program materials were coded and analyzed to identify common themes. The farmers valued these programs for providing knowledge and information, physical infrastructure (reduced startup costs), access to land, support and camaraderie, and collaborative action. These benefits arise because the farmers are in close proximity: they create a community of growers. Most farmers were comfortable farming leased land, felt that the program was worth the challenges, and said that they would not have their own farm operation otherwise. Lessons learned are drawn from the research and recommendations are made for establishing future programs in other locations, with a focus on Missoula, Montana

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

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