Snoring, an acoustic biomarker commonly observed in individuals with Obstructive Sleep Apnoea Syndrome (OSAS), holds significant potential for diagnosing and monitoring this recognized clinical disorder. Irrespective of snoring types, most snoring instances exhibit identifiable harmonic patterns manifested through distinctive energy distributions over time. In this work, we propose a novel method to differentiate monaural snoring from non-snoring sounds by analyzing the harmonic content of the input sound using harmonic/percussive sound source separation (HPSS). The resulting feature, based on the harmonic spectrogram from HPSS, is employed as input data for conventional neural network architectures, aiming to enhance snoring detection performance even under a limited data learning framework. To evaluate the performance of our proposal, we studied two different scenarios: 1) using a large dataset of snoring and interfering sounds, and 2) using a reduced training set composed of around 1% of the data material. In the former scenario, the proposed HPSS-based feature provides competitive results compared to other input features from the literature. However, the key advantage of the proposed method lies in the superior performance of the harmonic spectrogram derived from HPSS in a limited data learning context. In this particular scenario, using the proposed harmonic feature significantly enhances the performance of all the studied architectures in comparison to the classical input features documented in the existing literature. This finding clearly demonstrates that incorporating harmonic content enables more reliable learning of the essential time-frequency characteristics that are prevalent in most snoring sounds, even in scenarios where the amount of training data is limited.

翻译：鼾声作为阻塞性睡眠呼吸暂停综合征患者中常见的声学生物标志物，在诊断与监测这一公认临床病症方面具有重要潜力。无论鼾声类型如何，大多数鼾声实例均表现出可通过时域能量分布特征识别的谐波模式。本研究提出一种创新方法，通过谐波/打击乐声源分离技术分析输入声音的谐波成分，从而区分单声道鼾声与非鼾声。基于HPSS生成的谐波谱图特征被用作传统神经网络架构的输入数据，旨在有限数据学习框架下提升鼾声检测性能。为评估方案效能，我们研究了两种不同场景：1）使用包含鼾声与干扰声的大规模数据集；2）使用仅占原始数据约1%的缩减训练集。在前者场景中，相较于文献记载的其他输入特征，所提出的HPSS特征获得了具有竞争力的结果。然而，本方法的核心优势在于HPSS谐波谱图在有限数据学习情境中展现的卓越性能。在此特定场景下，与现有文献记载的经典输入特征相比，采用所提出的谐波特征能显著提升所有研究架构的性能。这一发现明确证明：即使训练数据有限，引入谐波成分仍能更可靠地学习大多数鼾声中普遍存在的关键时频特征。