Robust Radio Broadcast Monitoring Using a Multi-Band Spectral Entropy Signature

Camarena-Ibarrola, Antonio; Chávez, Edgar; Téllez, Eric Sadit

doi:10.1007/978-3-642-10268-4_69

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…The MEL-MBSES is based on the MBSES, an audio fingerprint formed with the entropy of the signal in 24 Barks sub-bands, which has been used in music information retrieval proving robustness to noise, equalization and loudness (22) To compute the MEL-MBSES, the signal is framed in 1024 samples with an overlap of 50%. After this, a Hann window is applied to every frame and then the N-point FFT is computed.…”

Section: Frame Level Feature: the Multiband Spectral Entropy Signaturmentioning

confidence: 99%

Scalable identification of mixed environmental sounds, recorded from heterogeneous sources

Beltrán

Chávez

Favela

2015

Pattern Recognition Letters

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Section: Frame Level Feature: the Multiband Spectral Entropy Signaturmentioning

confidence: 99%

Scalable identification of mixed environmental sounds, recorded from heterogeneous sources

Beltrán

Chávez

Favela

2015

Pattern Recognition Letters

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“…The same audio track was successfully used for automatic monitoring of radio stations in [4] with excellent results. We have now adapted this audio signature based on multi-band entropy (MBSES) for monitoring online musical performances, the resulting MBSES is determined as follows:…”

Section: Multi-band Spectral Entropy Signaturementioning

confidence: 93%

Online music tracking with global alignment

Camarena-Ibarrola

Chávez

2011

Int. J. Mach. Learn. & Cyber.

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To make audio monitoring, the state of the art in this area makes use of local alignment algorithms between the objective audio and musical interpretation.The inductive hypothesis of a local alignment tool is that the alignment is correct to the current position of an error this is drag and accumulate to subsequent errors which do not recover unless elaborate heuristics are used. Our approach uses a local non-alignment scheme based on the audio search the entire purpose of short segments of audio taken from musical performance to get the k nearest audio segments (the proximity is determined using audio tracks based on entropy signs).The current audio segment of the play is paired with the nearest (in time) between the k previously selected audio segments of the target audio.To our knowledge, this is the first algorithm able to start up from an arbitrary point in the audio end, for example, if the musical performance had already begun when the monitoring system just went on.We complemented the overall strategy through a simple heuristic of ignoring the candidates when they are all too far in time with respect to the last position reported by the system.We have tested our method with 62 musical pieces, some of which are pop and classical music mostly.For every song we have two interpretations, we use one as the audio object and the other as the interpretation which will be monitored.We obtained excellent results.Keywords Follow-up Á Music Á Entropy Á Index Á Near GroundsReal-time tracking of musical performances consists in setting the position on the target audio segment most recently captured audio of a musical performance as this is being played. Now explain briefly three applications of real-time tracking of musical performances: (a) The virtual music teacher, (b) auto-accompaniment of soloists, and (c) automatically added special effects in live performance. For the virtual master of music, the sound objective is wellexecuted piece of music by a professional musician. Musical performance which will be followed is that produced by the student. A music teacher teaches only one student at a time, as the student plays his instrument, the teacher makes comments pointing out errors or endorse the interpretation. A virtual teacher should do the same, use the audio signal generated by the student, and processed in real time and provide appropriate instructions to the student. The advantage of a virtual teacher that can generate many instances of the same and therefore simultaneously serve many students. For the automatic accompaniment of a singer or a solo audio music performance, the target audio would be made possibly in a recording studio, while the music that will be followed up is the live performance of the musician or singer. The system plays music accompaniment while generating activities (e.g., introducing delays in background music), these systems are true An earlier version of this work was presented at

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“…In this feature, the entropy is calculated for every of 24 Bark bands per frame and then converted to a binary signature [17]. Below we provide a more detailed explanation of the feature.…”

Section: Previous Workmentioning

confidence: 99%

Environmental Sound Recognition by Measuring Significant Changes in the Spectral Entropy

Beltrán-Márquez

Chávez²,

Favela

2012

Lecture Notes in Computer Science

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Abstract. Automatic identification of activities can be used to provide information to caregivers of persons with dementia for identifying assistance needs. Environmental audio provides significant and representative information of the context, making microphones a choice to identify activities automatically. However, in real situations, the audio captured by microphones comes from overlapping sound sources, making its identification a challenge for audio analysis and retrieval. In this paper we propose a succinct representation of the signal by measuring the multiband spectral entropy of the signal frame by frame, followed by a cosine transform and binary codification, we call this the Cosine Multi-Band Spectral Entropy Signature (CMBSES). To test our proposal, we created a database of a mix-up of triples from a collection of nine environmental sounds in four different signal-to-noise ratios (SNR). We codified both the original sounds and the triples and then searched all the original sounds in the mix-up collection. To establish a ground truth we also tested the same database with 48 people of assorted ages. Our feature extraction outperforms the state-of-the-art Mel Frequency Cepstral Coefficients (MFCC) and it also surpass humans in the experiment.

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Robust Radio Broadcast Monitoring Using a Multi-Band Spectral Entropy Signature

Cited by 10 publications

References 8 publications

Scalable identification of mixed environmental sounds, recorded from heterogeneous sources

Scalable identification of mixed environmental sounds, recorded from heterogeneous sources

Online music tracking with global alignment

Environmental Sound Recognition by Measuring Significant Changes in the Spectral Entropy

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