Papers for

acoustic monitoring teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Dual channel encoding improves understanding of sperm whale communication

Dual-Channel Contrastive Encoding of Sperm Whale Coda

Abstract: Sperm whales (Physeter macrocephalus) communicate through short click sequences called codas, whose structure includes the rhythmic pattern of inter-click intervals (ICI) and the spectral composition of the clicks. In this paper, we propose the Dual-Channel Contrastive Encoder (DCCE), which explicitly encodes rhythm and spectral information with separate encoders before fusing them. We evaluate DCCE on three coda-understanding tasks: social unit, coda type, and individual identification. We compare DCCE against strong baselines from literature under a common linear-probe protocol and analyze the role of each channel and training objective through ablations. On 1,383 codas, shared partitions and validation-only layer selection support three random splits and three recording-date-disjoint folds. DCCE with unit pairing and type supervision achieves mean random-split macro-F1 of 0.901/0.865/0.597 for unit/type/identity. Raw ICI remains strongest for type (0.987), and ICI plus temporal mel statistics is a strong identity reference (0.724). An auxiliary identity objective improves spectral-branch identity prediction but does not improve the mean fused-identity score in this configuration. Date-grouped evaluation and four year holdouts expose generalization limits. The results support task-dependent combinations of timing and spectral information, without establishing independent biological channels or general architectural superiority. Code, notebooks, and complete results are available at https://dcce-research-site.vercel.app.

Wed 21 OctSoundArtificial IntelligenceMachine Learning

The gist

Sperm whales talk using short sequences of clicks called codas that have unique rhythms and sound features. The authors designed a new AI method that separately studies these rhythms and sound patterns then combines them to understand whale social groups, types of messages, and individual whales. They tested their method and found it works well for identifying group and message types but can struggle to recognize individual whales over years. Their results suggest combining timing and sound details helps depending on the task, but no single way is the best for all.

Open → 2610.29101v1

Audio feature selection improves few-shot learning in shifted settings

Sample-Conditioned Representation Selection for Audio Few-Shot Learning

Abstract: Few-shot audio classifiers may rely on foreground-background co-occurrences and fail when those correlations shift. On SpurAudio, the resulting representation shift is concentrated and class dependent: for ResNet12, the top 10 percent of channels explain 82.80 percent of the null-corrected shift contribution. We propose SAMPLESELECT, which predicts a fixed-budget feature mask independently for each input while keeping the encoder and source classifier frozen. Training uses differentiable Gumbel Top-k selection with foreground classification and cross-background contrastive losses; inference uses deterministic Top-k masks and support-only linear adaptation. Across ResNet12 and Conv64 in 5-way 1-shot and 5-shot evaluation, SAMPLESELECT gives the best OOD accuracy among the compared methods and improves the matched full-representation control by 4.90-8.38 percentage points. Ablations and representation analyses further support the learned selection mechanism. Code is available at https://github.com/Cross-Innovation-Lab/SAMPLESELECT/

Tue 15 SeptArtificial IntelligenceSound

The gist

Few-shot audio classifiers sometimes get confused when sounds in the background change or mix with the target sound in new ways. The authors found that for a common audio model, only a small number of features explain most of these confusing shifts. They created a method called SAMPLESELECT that picks important features for each sound example without retraining the whole model. This method improves how well the model recognizes new sounds, especially when backgrounds change, by focusing only on the useful parts of the audio representation.

Open → 2609.17076v1