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Environmental Sound Recognition by Measuring Significant Changes in the Spectral Entropy

机译：通过测量频谱熵的重大变化来识别环境声音

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摘要

Automatic identification of activities can be used to provide information to caregivers of persons with dementia for identifying assistance needs. Environmental audio provides significant and representative information of the context, making microphones a choice to identify activities automatically. However, in real situations, the audio captured by microphones comes from overlapping sound sources, making its identification a challenge for audio analysis and retrieval. In this paper we propose a succinct representation of the signal by measuring the multi-band spectral entropy of the signal frame by frame, followed by a cosine transform and binary codification, we call this the Cosine Multi-Band Spectral Entropy Signature (CMBSES). To test our proposal, we created a database of a mix-up of triples from a collection of nine environmental sounds in four different signal-to-noise ratios (SNR). We codified both the original sounds and the triples and then searched all the original sounds in the mix-up collection. To establish a ground truth we also tested the same database with 48 people of assorted ages. Our feature extraction outperforms the state-of-the-art Mel Frequency Cepstral Coefficients (MFCC) and it also surpass humans in the experiment.
机译：活动的自动识别可用于向痴呆症患者的看护人提供信息，以识别援助需求。环境音频可提供重要的背景信息，使麦克风成为自动识别活动的选择。但是，在实际情况下，麦克风捕获的音频来自重叠的声源，使其识别成为音频分析和检索的挑战。在本文中，我们通过逐帧测量信号的多频带频谱熵，然后进行余弦变换和二进制编码，提出了一种简洁的信号表示方法，我们将其称为余弦多频带谱熵签名（CMBSES）。为了测试我们的建议，我们创建了一个数据库，其中包含来自四种不同信噪比（SNR）的九种环境声音的三重音的混合。我们将原始声音和三重声音都编成代码，然后在混合集合中搜索所有原始声音。为了建立基本事实，我们还用48个不同年龄的人测试了相同的数据库。我们的特征提取性能优于最新的梅尔频率倒谱系数（MFCC），并且在实验中也超过了人类。

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