标题： 特定任务的音频编码：机器学习的潜在特征是该机器的代码 显示英文标题

标题： Task-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine

摘要： 神经音频编解码器，利用量化算法，在各种语音/音频任务中产生了重大影响。 虽然高保真重建对于人类感知至关重要，但面向机器的音频编码（ACoM）优先考虑高效的压缩和下游任务性能，而不考虑感知细节。 本工作介绍了一种高效的ACoM方法，可以压缩和量化已训练的语音/音频下游模型的任何选定中间特征表示。 我们的方法结合了任务特定的损失指导和残差矢量量化（RVQ）损失，以极低的比特率（即低于200 bps）提供对下游模型性能的最小损失。 生成的分词器可适应各种比特率和模型大小，便于灵活部署。 在自动语音识别和音频分类上进行评估，我们的方法通过适当的正则化展示了其有效性和在更广泛任务和架构应用中的潜力。 显示英文摘要

摘要： Neural audio codecs, leveraging quantization algorithms, have significantly impacted various speech/audio tasks. While high-fidelity reconstruction is paramount for human perception, audio coding for machines (ACoM) prioritizes efficient compression and downstream task performance, disregarding perceptual nuances. This work introduces an efficient ACoM method that can compress and quantize any chosen intermediate feature representation of an already trained speech/audio downstream model. Our approach employs task-specific loss guidance alongside residual vector quantization (RVQ) losses, providing ultra-low bitrates (i.e., less than 200 bps) with a minimal loss of the downstream model performance. The resulting tokenizer is adaptable to various bitrates and model sizes for flexible deployment. Evaluated on automatic speech recognition and audio classification, our method demonstrates its efficacy and potential for broader task and architectural applicability through appropriate regularization.