Tempo-oriented music recommendation system based on human activity recognition using accelerometer and gyroscope data

IF 0.2 Q4 ACOUSTICS
Seung-Su Shin, Gi Yong Lee, Hyoung‐Gook Kim
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引用次数: 0

Abstract

In this paper, we propose a system that recommends music through tempo-oriented music classification and sensor-based human activity recognition. The proposed method indexes music files using tempo-oriented music classification and recommends suitable music according to the recognized user’s activity. For accurate music classification, a dynamic classification based on a modulation spectrum and a sequence classification based on a Mel-spectrogram are used in combination. In addition, simple accelerometer and gyroscope sensor data of the smartphone are applied to deep spiking neural networks to improve activity recognition performance. Finally, music recommendation is performed through a mapping table considering the relationship between the recognized activity and the indexed music file. The experimental results show that the proposed system is suitable for use in any practical mobile device with a music player.
基于加速度计和陀螺仪数据的人体活动识别的节奏音乐推荐系统
在本文中,我们提出了一个基于节奏的音乐分类和基于传感器的人类活动识别的音乐推荐系统。该方法采用面向节奏的音乐分类对音乐文件进行索引,并根据识别到的用户活动推荐合适的音乐。为了实现准确的音乐分类,将基于调制谱的动态分类和基于梅尔谱图的序列分类相结合。此外,将智能手机的简单加速度计和陀螺仪传感器数据应用于深度尖峰神经网络,以提高活动识别性能。最后,考虑到识别的活动和索引的音乐文件之间的关系,通过映射表执行音乐推荐。实验结果表明,该系统适用于任何带有音乐播放器的实际移动设备。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.60
自引率
50.00%
发文量
1
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