Evidence map›Paper›PMID 34961788›Full record

ArticleScientific reports2021

Utilizing DeepSqueak for automatic detection and classification of mammalian vocalizations: a case study on primate vocalizations.

Daniel Romero-Mujalli, Tjard Bergmann, Axel Zimmermann, Marina Scheumann

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. Sex differences in the impact of social relationships on individual vocal signatures in grey mouse lemurs (Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Daniel Romero-MujalliInstitute of Zoology, University of Veterinary Medicine Hannover, Bünteweg 17, 30559, Hannover, Germany. danielrm84@gmail.com.
Tjard BergmannInstitute of Zoology, University of Veterinary Medicine Hannover, Bünteweg 17, 30559, Hannover, Germany.
Axel ZimmermannUniversity of Aalen, Aalen, Germany.
Marina ScheumannInstitute of Zoology, University of Veterinary Medicine Hannover, Bünteweg 17, 30559, Hannover, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bioacoustic analyses of animal vocalizations are predominantly accomplished through manual scanning, a highly subjective and time-consuming process. Thus, validated automated analyses are needed that are usable for a variety of animal species and easy to handle by non-programing specialists. This study tested and validated whether DeepSqueak, a user-friendly software, developed for rodent ultrasonic vocalizations, can be generalized to automate the detection/segmentation, clustering and classification of high-frequency/ultrasonic vocalizations of a primate species. Our validation procedure showed that the trained detectors for vocalizations of the gray mouse lemur (Microcebus murinus) can deal with different call types, individual variation and different recording quality. Implementing additional filters drastically reduced noise signals (4225 events) and call fragments (637 events), resulting in 91% correct detections (N

Indexed as

Vocalization, AnimalAnimalsCheirogaleidaeMachine LearningSoftwareUltrasonics

Identifiers

PMID34961788
PMCPMC8712519

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.