Peter Grosche

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	All	Since 2018
Citations	1549	726
h-index	20	15
i10-index	30	21

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Meinard MüllerInternational Audio Laboratories Erlangen, Friedrich-Alexander-Universität Erlangen-NürnbergVerified email at audiolabs-erlangen.de
Joan SerràDolby LaboratoriesVerified email at dolby.com
Jürgen GeigerHarman Becker Automotive Systems GmbHVerified email at tum.de
Nanzhu JiangPhD candidate, International Audio Laboratories Erlangen, Friedrich-Alexander-Universität ErlangenVerified email at audiolabs-erlangen.de
Stefan WeinzierlProfessor für Audiokommunikation, TU BerlinVerified email at tu-berlin.de
Fabian BrinkmannResearch Associate at Audio Communication Group, TU BerlinVerified email at tu-berlin.de
Sebastian EwertSpotifyVerified email at qmul.ac.uk
Frans WieringUtrecht UniversityVerified email at uu.nl
Frank KurthFraunhofer FKIE, apl. Professor für Informatik, Universität BonnVerified email at fkie.fraunhofer.de
Björn SchullerProfessor of AI, Imperial College London & University of Augsburg & CSO, audEERINGVerified email at imperial.ac.uk
Christian Schörkhuberatmoky GmbH | sonible GmbH | Institute of Electronic Music and AcousticsVerified email at atmoky.com
Franz ZotterAssoc. Prof., University of Music and Performing Arts GrazVerified email at iem.at
Karim HelwaniAmazonVerified email at ieee.org
Gerhard RigollProfessor for Human-Machine Communication, TU MünchenVerified email at gerhard-rigoll.de
Hendrik Schreibertagtraum industries incorporatedVerified email at tagtraum.com
Felix WeningerNuance CommunicationsVerified email at nuance.com
Stine HarderTechnical University of Denmark Verified email at dtu.dk
Rasmus R. PaulsenAssociate Professor in Medical Image Analysis, DTU Compute, Technical University of DenmarkVerified email at dtu.dk
Heinrich W. LöllmannFAU Erlangen-NurembergVerified email at lnt.de
Mikko ParviainenTampere UniversityVerified email at tuni.fi

Peter Grosche

Huawei

Verified email at huawei.com - Homepage

Audio Signal Processing Audio Data Science Music Information Retrieval AI


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High resolution audio synchronization using chroma onset features S Ewert, M Müller, P Grosche Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE …, 2009	203	2009
Extracting predominant local pulse information from music recordings P Grosche, M Muller IEEE Transactions on Audio, Speech, and Language Processing 19 (6), 1688-1701, 2010	113	2010
Cyclic tempogram—a mid-level tempo representation for musicsignals P Grosche, M Müller, F Kurth 2010 IEEE International Conference on Acoustics, Speech and Signal …, 2010	107	2010
Audio content-based music retrieval P Grosche, M Müller, J Serra Dagstuhl Follow-Ups 3, 2012	104	2012
Unsupervised Music Structure Annotation by Time Series Structure Features and Segment Similarity J Serrà, M Müller, P Grosche, JL Arcos IEEE TRANSACTIONS ON MULTIMEDIA 16 (5), 1229-1240, 2014	101	2014
Unsupervised detection of music boundaries by time series structure features J Serra, M Müller, P Grosche, JL Arcos Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 1613-1619, 2012	90	2012
What Makes Beat Tracking Difficult? A Case Study on Chopin Mazurkas. P Grosche, M Müller, CS Sapp ISMIR, 649-654, 2010	73	2010
Analyzing Chroma Feature Types for Automated Chord Recognition N Jiang, P Grosche, V Konz, M Müller Proc. of 42nd AES Conference, 2011	66	2011
A cross-evaluated database of measured and simulated HRTFs including 3D head meshes, anthropometric features, and headphone impulse responses F Brinkmann, M Dinakaran, R Pelzer, P Grosche, D Voss, S Weinzierl Journal of the Audio Engineering Society 67 (9), 705-718, 2019	63	2019
Tempogram toolbox: Matlab implementations for tempo and pulse analysis of music recordings P Grosche, M Müller Proceedings of the 12th International Conference on Music Information …, 2011	56	2011
A robust fitness measure for capturing repetitions in music recordings with applications to audio thumbnailing M Muller, N Jiang, P Grosche IEEE Transactions on audio, speech, and language processing 21 (3), 531-543, 2012	47	2012
A Mid-Level Representation for Capturing Dominant Tempo and Pulse Information in Music Recordings. P Grosche, M Müller ISMIR, 189-194, 2009	44	2009
A Segment-based Fitness Measure for Capturing Repetitive Structures of Music Recordings M Müller, P Grosche, N Jiang 12th International Conference on Music Information Retrieval, 615-620, 2011	42	2011
Toward characteristic audio shingles for efficient cross-version music retrieval P Grosche, M Müller Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International …, 2012	40	2012
Structure-Based Audio Fingerprinting for Music Retrieval. P Grosche, J Serrà, M Müller, JL Arcos ISMIR, 55-60, 2012	38	2012
Robust segmentation and annotation of folk song recordings M Müller, P Grosche, F Wiering Untitled Event, 735-740, 2009	33	2009
Signal processing apparatus for enhancing a voice component within a multi-channel audio signal J Geiger, P Grosche US Patent 10,210,883, 2019	24	2019
Computing predominant local periodicity information in music recordings P Grosche, M Müller Applications of Signal Processing to Audio and Acoustics, 2009. WASPAA'09 …, 2009	23	2009
Automated analysis of performance variations in folk song recordings M Müller, P Grosche, F Wiering Proceedings of the international conference on Multimedia information …, 2010	21	2010
Method and mobile device for processing an audio signal P Grosche, Y Lang US Patent 9,949,053, 2018	20	2018

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