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Lukas Grinewitschus

dblp:304/8227 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2022
0000-0002-6028-3781ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Audio and music processing · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Audio and music processing › music transcription
multipitch estimation
0.612022
The Harmonic Shift Algorithm for Efficient Multi-Pitch Detection · IEEE ACM Trans. Audio Speech Lang. Process. 2022
Audio and music processing › time-frequency analysis
constant-q transform
0.212022
The Harmonic Shift Algorithm for Efficient Multi-Pitch Detection · IEEE ACM Trans. Audio Speech Lang. Process. 2022

Methods — techniques the papers use, named apart from their topics

noncoherent blind detection · 0.6constant-q transform · 0.6
YearPublicationVenuePosition
2022 The Harmonic Shift Algorithm for Efficient Multi-Pitch Detection
abstract
Existing approaches for multi-pitch detection have still been highly complex. In this paper, the harmonic shift algorithm (HSA) for multi-pitch detection is illustrated. The HSA is a noncoherent blind detection scheme, exploiting the constant-Q transform (CQT). The only requirement for the successful application of the HSA is that all the sources are harmonic sources. Due to this simplicity, the HSA is considered viable for both single-pitch and multi-pitch detection, making its attractive for the application in other areas of signal processing. When illustrating the performance capabilities of the HSA, we demonstrate that it outperforms the state of the art regarding the computational complexity providing detection precision, accuracy and a recall factor comparable with existing schemes.
Lukas Grinewitschus, Peter Jung 0002
IEEE ACM Trans. Audio Speech Lang. Process.1