Institute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, UK.
J Acoust Soc Am. 2011 Aug;130(2):893-903. doi: 10.1121/1.3609117.
This paper proposes an adaptive filter-based method for detection and frequency estimation of whistle calls, such as the calls of birds and marine mammals, which are typically analyzed in the time-frequency domain using a spectrogram. The approach taken here is based on adaptive notch filtering, which is an established technique for frequency tracking. For application to automatic whistle processing, methods for detection and improved frequency tracking through frequency crossings as well as interfering transients are developed and coupled to the frequency tracker. Background noise estimation and compensation is accomplished using order statistics and pre-whitening. Using simulated signals as well as recorded calls of marine mammals and a human whistled speech utterance, it is shown that the proposed method can detect more simultaneous whistles than two competing spectrogram-based methods while not reporting any false alarms on the example datasets. In one example, it extracts complete 1.4 and 1.8 s bottlenose dolphin whistles successfully through frequency crossings. The method performs detection and estimates frequency tracks even at high sweep rates. The algorithm is also shown to be effective on human whistled utterances.
本文提出了一种基于自适应滤波器的方法，用于检测和估计口哨声（如鸟类和海洋哺乳动物的叫声）的频率，这些声音通常在时频域中使用声谱图进行分析。这里采用的方法基于自适应陷波滤波，这是一种用于频率跟踪的成熟技术。为了应用于自动口哨处理，开发了通过频率交叉和干扰瞬态进行检测和改进频率跟踪的方法，并将其与频率跟踪器耦合。使用顺序统计和预白化进行背景噪声估计和补偿。使用模拟信号以及记录的海洋哺乳动物叫声和人类口哨语音，结果表明，与两种竞争的基于声谱图的方法相比，所提出的方法可以检测到更多同时发出的口哨声，而在示例数据集中没有报告任何误报。在一个示例中，它通过频率交叉成功提取了完整的 1.4 和 1.8 秒宽吻海豚口哨声。该方法甚至在高扫描速率下也能进行检测和估计频率跟踪。该算法在人类口哨语音中也表现出了有效性。
