Zero-Shot Audio Classification Via Semantic Embeddings

Tutkimustuotos: ArtikkeliScientificvertaisarvioitu

Abstrakti

In this paper, we study zero-shot learning in audio classification via semantic embeddings extracted from textual labels and sentence descriptions of sound classes. Our goal is to obtain a classifier that is capable of recognizing audio instances of sound classes that have no available training samples, but only semantic side information. We employ a bilinear compatibility framework to learn an acoustic-semantic projection between intermediate-level representations of audio instances and sound classes, i.e., acoustic embeddings and semantic embeddings. We use VGGish to extract deep acoustic embeddings from audio clips, and pre-trained language models (Word2Vec, GloVe, BERT) to generate either label embeddings from textual labels or sentence embeddings from sentence descriptions of sound classes. Audio classification is performed by a linear compatibility function that measures how compatible an acoustic embedding and a semantic embedding are. We evaluate the proposed method on a small balanced dataset ESC-50 and a large-scale unbalanced audio subset of AudioSet. The experimental results show that classification performance is significantly improved by involving sound classes that are semantically close to the test classes in training. Meanwhile, we demonstrate that both label embeddings and sentence embeddings are useful for zero-shot learning. Classification performance is improved by concatenating label/sentence embeddings generated with different language models. With their hybrid concatenations, the results are improved further.

AlkuperäiskieliEnglanti
Sivut1233-1242
Sivumäärä10
JulkaisuIEEE/ACM Transactions on Audio Speech and Language Processing
Vuosikerta29
DOI - pysyväislinkit
TilaJulkaistu - 2021
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Julkaisufoorumi-taso

  • Jufo-taso 3

!!ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Acoustics and Ultrasonics
  • Computational Mathematics
  • Electrical and Electronic Engineering

Sormenjälki

Sukella tutkimusaiheisiin 'Zero-Shot Audio Classification Via Semantic Embeddings'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä