arXiv cs.AI· Mudit Sinha, Sanika Chavan·· 14 小时前AI 评分19
冻结音频编码器如何比较抹香鲸、人类语音、孟加拉雀与狨猴叫声的声学结构
Structure Across Voices: Comparing acoustic-event type accumulation and sequence dependence across four vocal corpora using frozen audio encoders
AI 导读
研究提出一套匹配流程,用冻结音频编码器为抹香鲸 coda、人类语音音素、孟加拉雀音节和普通狨猴叫声提供统一声学表示,并通过匹配采样让各语料获得同等证据量。结果显示四类语料模式各异:抹香鲸录音中新声学形式持续出现且序列高度组织化、重复性强,雀鸟录音的声音时间组织最强,尤其体现在相邻声音与重复模式的排列上。直接的时域与频谱测量证实这些差异也存在于录音本身。
正文 · 原文
Abstract:Comparing acoustic structure across very different vocal corpora is difficult because the recordings use different native event units, contain different amounts of sequence, and were collected under different conditions. We develop a matched procedure for comparing such corpora without forcing them into the same event definition. Frozen audio encoders provide a common acoustic representation for recorded events, while matched sampling gives each corpus the same amount of evidence for acoustic and sequence analysis. We apply this procedure to sperm-whale codas, human speech phones, Bengalese-finch syllables, and common-marmoset calls. The comparison reveals different patterns across the four corpora. Sperm-whale recordings stand out broadly: new acoustic forms continue to appear, while their sequences also show strong organization and repetition. Finch recordings show the strongest organization of sounds over time, especially in how neighboring sounds and repeated patterns are arranged. Direct temporal and spectral measurements show that these differences are also present in the recorded sounds themselves. The comparison provides a practical way to measure acoustic diversity and temporal organization across very different vocal corpora by combining a common acoustic representation with matched observation opportunity, without treating their native sounds as equivalent.
| Comments: | 12 pages, 4 figures, 2 tables |
| Subjects: | Sound (cs.SD); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.16612 [cs.SD] |
| (or arXiv:2609.16612v2 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2609.16612 arXiv-issued DOI via DataCite |
Submission history
From: Mudit Sinha [view email]
[v1]
Tue, 15 Sep 2026 04:19:03 UTC (159 KB)
[v2]
Sat, 24 Oct 2026 04:10:16 UTC (936 KB)
来源:arXiv cs.AI · arxiv.org