cs.SD · 2026-07-23 · No. 62

Sound, 2026-07-23.

4 new papers in cs.SD. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

4 entries
  1. 01

    Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering

    Junyu Dai, Xinyue Fan, Weiqin Li, Xiangang Li, Yunjia Li, Bin Ma, Yukun Ma, Chongjia Ni, Yufei Shi, Haoxu Wang,...

    cs.SD · cs.AI · eess.AS

    In this report, we present a unified song generation framework capable of producing high-quality full-length music from lyrics, text descriptions, and musical attributes. The proposed framework supports three tasks: Lyrics-to-Song Generation, which generates complete songs from text descriptions, lyrics, and musical attributes; Instrumental Music Generation, which creates music without vocals; and Cover Song Generation, which reinterprets...

    arxiv.org/abs/2607.20253 · PDF

  2. 02

    Audio-Zero: Label-Free Self-Evolution for Fine-Grained Audio Reasoning

    Siqian Tong, Xuan Li, Chaozhuo Li, Baolong Bi, Yiwei Wang, Yujun Cai, Shenghua Liu, Chengpeng Hao

    cs.SD · cs.AI

    Large Audio Language models (LALMs) have made rapid progress on acoustic understanding, yet they still struggle with fine-grained audio reasoning (e.g., recognizing event order, repetitions and duration). Existing post-training methods heavily rely on expensive external labels or provide only coarse semantic signals. To bridge this gap, we introduce Audio-Zero, the first label-free self-evolution framework in the field of LALMs that improves...

    arxiv.org/abs/2607.20166 · PDF

  3. 03

    Cumsum-Composable Phase Transport for Low-Cost Streaming Keyword Spotting

    Mahesh Godavarti

    cs.SD · cs.LG

    State-space sequence models are attractive for streaming speech because they maintain compact recurrent state, but scan-style training kernels can have unfavorable constants for short audio tasks. We study cumsum-composable phase transport, a streaming-native temporal layer for keyword spotting. Each layer projects acoustic frames to complex channels, transports them by learned unitary rotations, accumulates a finite window using prefix...

    arxiv.org/abs/2607.20086 · PDF

  4. 04

    RPPNet: Perceptually-Grouped Rhythm-Pitch Primitives for Long-Term Structure Melody Generation via Boundary-Aware Modeling

    Tieyao Zhang, Yuke Liu, Jiaxing Yu, Xinda Wu, Kejun Zhang, Genfang Chen

    cs.SD · cs.AI

    Existing symbolic music generation models typically use bars as the basic structural unit. However, human perception of musical phrases often does not align with notated bar lines, leading to long-term structural fragmentation. This paper proposes RPPNet-a two-stage deep learning architecture with variable structural boundaries. It first generates variable-length Rhythm-Pitch Primitive (RPP) sequences, where each RPP encodes note count,...

    arxiv.org/abs/2607.19776 · PDF

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