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Apple ML Research tackles long-form audio decoding challenges

By PulseAugur Editorial · [1 sources] ·

Apple Machine Learning Research has published a paper detailing Segmental Attention Decoding with Long Form Acoustic Encodings. This research addresses the limitations of attention-based encoder-decoder models when processing long audio segments by proposing four modifications. These include injecting positional encodings, using extended acoustic context for training, segment concatenation, and semantic segmentation to improve accuracy and enable auto-regressive decoding. AI

IMPACT Improves capabilities for processing long audio inputs in AI models.

RANK_REASON The cluster contains a research paper from a major tech company's ML research division. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Apple Machine Learning Research →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Apple ML Research tackles long-form audio decoding challenges

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The cluster contains a research paper from a major tech company's ML research division. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Apple Machine Learning Research TIER_1 English(EN) ·

    Segmental Attention Decoding with Long Form Acoustic Encodings

    We address the fundamental incompatibility of attention-based encoder-decoder (AED) models with long-form acoustic encodings. AED models trained on segmented utterances learn to encode absolute frame positions by exploiting limited acoustic context beyond segment boundaries, but …

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