Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, USA.
Helen Wills Neuroscience Institute, UC Berkeley, Berkeley, CA, 94720, USA.
Nat Commun. 2018 May 29;9(1):2122. doi: 10.1038/s41467-018-04551-8.
The "cocktail party problem" requires us to discern individual sound sources from mixtures of sources. The brain must use knowledge of natural sound regularities for this purpose. One much-discussed regularity is the tendency for frequencies to be harmonically related (integer multiples of a fundamental frequency). To test the role of harmonicity in real-world sound segregation, we developed speech analysis/synthesis tools to perturb the carrier frequencies of speech, disrupting harmonic frequency relations while maintaining the spectrotemporal envelope that determines phonemic content. We find that violations of harmonicity cause individual frequencies of speech to segregate from each other, impair the intelligibility of concurrent utterances despite leaving intelligibility of single utterances intact, and cause listeners to lose track of target talkers. However, additional segregation deficits result from replacing harmonic frequencies with noise (simulating whispering), suggesting additional grouping cues enabled by voiced speech excitation. Our results demonstrate acoustic grouping cues in real-world sound segregation.
“鸡尾酒会问题”要求我们从混合的声源中辨别出各个声源。大脑必须利用对自然声音规律的了解来实现这一目标。一个备受讨论的规律是，频率往往具有谐和关系（基频的整数倍）。为了检验谐和性在现实世界声音分离中的作用，我们开发了语音分析/合成工具来干扰语音的载波频率，破坏谐波频率关系，同时保持决定音位内容的频谱时间包络。我们发现，谐和性的违反会导致语音的各个频率彼此分离，尽管单个语音的可理解性保持不变，但会损害同时发出的语音的可理解性，并导致听众无法跟踪目标说话者。然而，用噪声（模拟低语）替代谐波频率会导致额外的分离缺陷，这表明由浊音语音激励提供了其他分组线索。我们的结果证明了现实世界声音分离中的声学分组线索。
