Encoder-Side Neuron Identification and Amplification for Acoustic Perception in Large Audio-Language Models

Noosaga Placements

• Artificial Intelligencesubfield90%
  The paper addresses a core AI problem: improving the perceptual capabilities of large audio-language models through a novel inference-time intervention method. It combines techniques from deep learning, natural language processing, and audio understanding within an AI framework.
  Large audio-language models (LALMs) often underperform on fine-grained, non-semantic attributes of speech...We introduce IAAN, Identifying and Amplifying Acoustic Neurons, a training-free and label-free method...These results show that a small, precisely targeted intervention inside the audio encoder is an effective and largely untapped way to strengthen the acoustic understanding of LALMs...
• Deep Learningframework90%
  The paper directly uses deep learning models (transformer-based audio encoders and LALMs) and applies neuron-level analysis and manipulation within these models. The method is built on top of pre-trained deep neural networks.
  A transformer block applies multi-head attention followed by a two-layer feed-forward network (FFN)...Since the audio encoders are transformers, applying this definition to an encoder with L layers gives N = L d_ff neurons...IAAN scores each feed-forward neuron in the audio encoder by contrasting its activation on the real waveform with that on a noise reference...
• Deep Learningsubfield90%
  The method operates on deep neural network components (feed-forward neurons in transformer-based audio encoders) and leverages deep learning concepts such as activation analysis, neuron-level intervention, and inference-time manipulation of learned representations.
  We define each coordinate a_i = φ(W_1 h)_i of the FFN hidden activation a = φ(W_1 h) ∈ R^{d_ff}... as a neuronIAAN scores each feed-forward neuron in the audio encoder by contrasting its activation on the real waveform with that on a noise reference...The audio encoder runs two forward passes, one on the real audio waveform and one on a noise reference...
• Foundation Modelsframework80%
  The paper is situated within the context of large audio-language models (LALMs), which are foundation models for audio understanding. The method is applied to pre-trained LALMs without retraining, leveraging their existing capabilities.
  Large audio-language models (LALMs) build on a pretrained large language model (LLM) backbone with an audio encoder...We evaluate IAAN on three open-source LALMs, Audio-Flamingo-3, Qwen2.5-Omni, and Kimi-Audio...IAAN is a training-free and label-free method that scores each feed-forward neuron in the audio encoder...
• Transformer Architectureframework80%
  The audio encoders in the LALMs are transformer-based, and the method specifically targets feed-forward neurons within transformer blocks. The analysis of neuron distributions across encoder layers relies on the transformer architecture.
  A transformer block applies multi-head attention followed by a two-layer feed-forward network (FFN)...Since the audio encoders are transformers, applying this definition to an encoder with L layers gives N = L d_ff neurons...Fig. 2b indicates the selected neurons are unevenly distributed across encoder layers.
• Natural Language Processingsubfield70%
  The work is situated in the context of large audio-language models (LALMs), which extend large language models (LLMs) to the auditory modality. The method improves the acoustic grounding of these models, which is relevant to multimodal NLP.
  Large audio-language models (LALMs) build on a pretrained large language model (LLM) backbone with an audio encoder...Our goal is to strengthen this weaker side, making LALMs perceive acoustic detail in the voice more reliably without retraining.We evaluate IAAN on three open-source LALMs, Audio-Flamingo-3, Qwen2.5-Omni, and Kimi-Audio...
• Interpretabilityframework60%
  The paper involves identifying which neurons are responsible for acoustic perception, which is a form of mechanistic interpretability. The method scores neurons based on their response to acoustic information, providing insight into the internal workings of the encoder.
  IAAN scores each feed-forward neuron in the audio encoder by contrasting its activation on the real waveform with that on a noise reference...The improvement also depends on which specific neurons are amplified, not merely on their number, confirming that IAAN's acoustic score succeeds in identifying the neurons that matter.Fig. 2a shows that the acoustic score is sharply concentrated near zero... Only a small tail breaks away from this majority.

Abstract

Large audio-language models (LALMs) often underperform on fine-grained, non-semantic attributes of speech, such as a speaker's emotion, despite strong performance on speech content. Improving this without the cost of retraining calls for an effective inference-time intervention, yet most existing methods intervene only after the audio encoder and operate at a relatively coarse granularity. The encoder itself, where acoustic information is first extracted from the waveform, remains largely unexplored, especially at the level of individual neurons. We introduce IAAN, Identifying and Amplifying Acoustic Neurons, a training-free and label-free method that scores each feed-forward neuron in the audio encoder by contrasting its activation on the real waveform with that on a noise reference lacking the real audio's acoustic information. IAAN then amplifies a small set of the highest-scoring neurons at inference. Across ten non-semantic speech attributes, IAAN improves average accuracy by 25.7 points on Audio-Flamingo-3, 21.4 on Qwen2.5-Omni, and 9.7 on Kimi-Audio. It also improves a model already explicitly fine-tuned to prioritize acoustic evidence. In controlled comparisons, both the encoder locus and neuron-level selectivity prove necessary for this gain. Intervening after the encoder, at the decoding side or inside the language model, yields little to no improvement, or even deteriorates accuracy. The improvement also depends on which specific neurons are amplified, not merely on their number, confirming that IAAN's acoustic score succeeds in identifying the neurons that matter. These results show that a small, precisely targeted intervention inside the audio encoder is an effective and largely untapped way to strengthen the acoustic understanding of LALMs, opening a new direction for inference-time methods that improve acoustic perception through neuron-level access to the encoder.

Paper Context