Pith. sign in

REVIEW 1 cited by

autrainer: A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2412.11943 v2 pith:QQNBZKVZ submitted 2024-12-16 cs.SD cs.AIcs.LGeess.AS

Simon Rampp , Andreas Triantafyllopoulos , Manuel Milling , Björn W. Schuller This is my paper
classification cs.SDcs.AIcs.LGeess.AS
keywords autrainerauditioncomputertaskstrainingdeepextensiblelearning
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This work introduces the key operating principles for autrainer, our new deep learning training framework for computer audition tasks. autrainer is a PyTorch-based toolkit that allows for rapid, reproducible, and easily extensible training on a variety of different computer audition tasks. Concretely, autrainer offers low-code training and supports a wide range of neural networks as well as preprocessing routines. In this work, we present an overview of its inner workings and key capabilities.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Charting 15 years of progress in deep learning for speech emotion recognition: A replication study

    cs.SD 2025-08 conditional novelty 5.0 of 10

    Newer, larger deep learning models show no consistent gains over older architectures for speech emotion recognition across two naturalistic benchmarks, with results sensitive to model selection and hyperparameters.

Pith tools