Musical instrument recognition in user-generated videos using a multimodal convolutional neural network architecture

Slizovskaia, Olga; Gómez Gutiérrez, Emilia, 1975-; Haro Ortega, Gloria

doi:http://dx.doi.org/10.1145/3078971.3079002

Musical instrument recognition in user-generated videos using a multimodal convolutional neural network architecture

Citation

Slizovskaia O, Gómez E, Haro G. Musical instrument recognition in user-generated videos using a multimodal convolutional neural network architecture. In: ICMR 2017. ACM International Conference on Multimedia Retrieval; 2017 Jun 6-9; Bucharest, Romania. New York (NY): ACM; 2017. p. 226-32. DOI: 10.1145/3078971.3079002

Abstract

This paper presents a method for recognizing musical instruments in user-generated videos. Musical instrument recognition from music signals is a well-known task in the music information retrieval (MIR) field, where current approaches rely on the analysis of the good-quality audio material. This work addresses a real-world scenario with several research challenges, i.e. the analysis of user-generated videos that are varied in terms of recording conditions and quality and may contain multiple instruments sounding simultaneously and background noise. Our approach does not only focus on the analysis of audio information, but we exploit the multimodal information embedded in the audio and visual domains. In order to do so, we develop a Convolutional Neural Network (CNN) architecture which combines learned representations from both modalities at a late fusion stage. Our approach is trained and evaluated on two large-scale video datasets: YouTube-8M and FCVID. The proposed architectures demonstrate state-of-the-art results in audio and video object recognition, provide additional robustness to missing modalities, and remains computationally cheap to train.