一种阈值自适应调整的实时音频分割方法

基于内容的音频分析近年来引起了较多学者的关注.对自动分割方法进行了分析,分割是对音频信号进行深入分析的首要步骤,通常根据能量、幅值、基音等时域或频域的声音特征,设定若干固定阈值来实现.由于实时音频信号来源复杂,环境和采集设备的变化都会导致阈值的波动,从而直接影响到分割的质量.提出了一种基于声音背景学习的阈值初值计算方法,主要针对实时音频应用,设计了环境因子作为对外界环境进行检测的度量,并利用其自适应调节分割阈值,采用查表法,通过状态转移进行分片类型判断以在效率和精度之间取得平衡,并设计了多组分割实验对上述方法分别进行了验证.

Adaptive threshold method for real-time audio segmentation

Content-based audio analysis has become an interesting direction for many researchers. Deep analysis on audio signal segmentation was reviewed. Conventionally, automatic segmentation can be implemented by calculating some audio features like short-term energy, amplitude, fundamental frequency or others, in time-domain or frequency-domain, via referencing to several constant thresholds established in advance. But these methods were found lack of reliability in such applications, because of the complexity of real-time audio signals, together with the fluky changing of environment and various models of acquiring devices. An adaptive threshold adjusting method based on background learning was introduced. On condition of real-time environment, a so-called environment factor was computed iteratively through background learning, and then it was used as a measure to control the fluctuating of real thresholds. To make a balance between efficiency and precision, a state table was introduced to help judging on the types of audio clips. Validity of the methods was proved by a group of experiments.