Audio-Visual Tensor Fusion Network for Piano Player Posture Classification

Playing the piano in the correct position is important because the correct position helps to produce good sound and prevents injuries. Many studies have been conducted in the field of piano playing posture recognition that combines various techniques. Most of these techniques are based on analyzing visual information. However, in the piano education field, it is essential to utilize audio information in addition to visual information due to the deep relationship between posture and sound. In this paper, we propose an audio-visual tensor fusion network (simply, AV-TFN) for piano performance posture classification. Unlike existing studies that used only visual information, the proposed method uses audio information to improve the accuracy in classifying the postures of professional and amateur pianists. For this, we first introduce a dataset called C3Pap (Classic piano performance postures of amateur and professionals) that contains actual piano performance videos in diverse environments. Furthermore, we propose a data structure that represents audio-visual information. The proposed data structure represents audio information on the color scale and visual information on the black and white scale for representing relativeness between them. We call this data structure an audio-visual tensor. Finally, we compare the performance of the proposed method with state-of-the-art approaches: VN (Visual Network), AN (Audio Network), AVN (Audio-Visual Network) with concatenation and attention techniques. The experiment results demonstrate that AV-TFN outperforms existing studies and, thus, can be effectively used in the classification of piano playing postures.