Justin Salamon
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mir_eval wins best poster presentation at ISMIR 2014

30/10/2014

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Our paper  "mir_eval: A Transparent Implementation of Common MIR Metrics", lead and presented by fearless Colin Raffel has won the Best Poster Presentation Award at the ISMIR 2014 conference!
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Here's the paper's abstract:
Central to the field of MIR research is the evaluation of algorithms used to extract information from music data. We present mir_eval, an open source software library which provides a transparent and easy-to-use implementation of the most common metrics used to measure the performance of MIR algorithms. In this paper, we enumerate the metrics implemented by mir_eval and quantitatively compare each to existing implementations. When the scores reported by mir_eval differ substantially from the reference, we detail the differences in implementation. We also provide a brief overview of mir_eval’s architecture, design, and intended use.

A massive congratulations to comrades Colin, Brian, Eric, Oriol Dawen and Dan for creating this awesome project, and in particular to Colin for leading this initiative and doing a fantastic job at presenting it at ISMIR today!

You can check out mir_eval here: https://github.com/craffel/mir_eval
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Announcing the  Urban  Sound  dataset and taxonomy

23/10/2014

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 We are pleased to announce the release of UrbanSound, a dataset containing 27 hours of field-recordings with over 3000 labelled sound source occurrences from 10 sound classes. The dataset focuses on sounds that occur in urban acoustic environments.

To facilitate comparable research on urban sound source classification, we are also releasing a second version of this dataset, UrbanSound8K, with 8732 excerpts limited to 4 seconds (also with source labels), and pre-sorted into 10 stratified folds. In addition to the source ID both datasets also include a (subjective) salience label for each source occurrence: foreground / background.

The datasets are released for research purposes under a Creative Commons Attribution Noncommercial License, and are available online at the dataset companion website:


http://urbansounddataset.weebly.com/

This companion website also contains further information about each dataset, including the Urban Sound Taxonomy from which the 10 sound classes in this dataset were selected.

The datasets and taxonomy will be presented at the ACM Multimedia 2014 conference in Orlando in a couple of weeks. For those interested, please see our paper:

J. Salamon, C. Jacoby and J. P. Bello, "A Dataset and Taxonomy for Urban Sound Research", in Proc. 22nd ACM International Conference on Multimedia, Orlando USA, Nov. 2014.

For those attending ISMIR 2014 next week, I will also be there if you would like to discuss the datasets and taxonomy.

I hope you find the datasets useful for your work and look forward to seeing some of you at ISMIR and ACM-MM in the coming weeks!

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MeloSynth

16/10/2014

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Since we released the MELODIA vamp plugin implementing our melody extraction algorithm, I've been contacted a number of times by people interested in synthesizing the pitch sequences estimated by MELODIA, like the examples provided on my melody extraction and phd thesis pages.

To this end, I've written a small python script, MeloSynth, to do just that:
www.github.com/justinsalamon/melosynth

MeloSynth is written in Python, is open source, and requires Python and NumPy. It's designed to be as simple as possible to use, no programming/python knowledge required. Given a txt or csv file with two columns [timestamps, frequency], the default behavior is to synthesize a wav file using a single sinusoid. The script also has options for setting the sampling frequency, adding more harmonics, changing the waveform, synthesizing negative values (which are used to indicate the absence of pitch by convention) and batch processing all files in a folder.

MeloSynth can of course also be used to synthesize pitch estimates from other algorithms, as long as the output is provided in the expected double column format.

Give it a spin and let me know what you think :)

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