惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

有赞技术团队
有赞技术团队
小众软件
小众软件
The Cloudflare Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园_首页
酷 壳 – CoolShell
酷 壳 – CoolShell
爱范儿
爱范儿
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
雷峰网
雷峰网
Jina AI
Jina AI
博客园 - 【当耐特】
V
Visual Studio Blog
美团技术团队
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
罗磊的独立博客
大猫的无限游戏
大猫的无限游戏
量子位
IT之家
IT之家
G
Google Developers Blog
V
V2EX
The GitHub Blog
The GitHub Blog
月光博客
月光博客
GbyAI
GbyAI

cs.SD updates on arXiv.org

Mutual Forcing: Dual-Mode Self-Evolution for Fast Autoregressive Audio-Video Character Generation WhisperPipe: A Resource-Efficient Streaming Architecture for Real-Time Automatic Speech Recognition Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models PSP: An Interpretable Per-Dimension Accent Benchmark for Indic Text-to-Speech Praxy Voice: Voice-Prompt Recovery + BUPS for Commercial-Class Indic TTS from a Frozen Non-Indic Base at Zero Commercial-Training-Data Cost Korean aegyo speech shows systematic F1 increase to signal childlike qualities All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation RAS: a Reliability Oriented Metric for Automatic Speech Recognition Speech Enhancement Based on Drifting Models HeadRouter: Dynamic Head-Weight Routing for Task-Adaptive Audio Token Pruning in Large Audio Language Models Hallo-Live: Real-Time Streaming Joint Audio-Video Avatar Generation with Asynchronous Dual-Stream and Human-Centric Preference Distillation Talker-T2AV: Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling Robust Audio-Text Retrieval via Cross-Modal Attention and Hybrid Loss Spectro-Temporal Modulation Representation Framework for Human-Imitated Speech Detection UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions Do LLM Decoders Listen Fairly? Benchmarking How Language Model Priors Shape Bias in Speech Recognition Materialistic RIR: Material Conditioned Realistic RIR Generation SpeechParaling-Bench: A Comprehensive Benchmark for Paralinguistic-Aware Speech Generation ONOTE: Benchmarking Omnimodal Notation Processing for Expert-level Music Intelligence From Image to Music Language: A Two-Stage Structure Decoding Approach for Complex Polyphonic OMR ATIR: Towards Audio-Text Interleaved Contextual Retrieval Enhancing Speaker Verification with Whispered Speech via Post-Processing Environmental Sound Deepfake Detection Using Deep-Learning Framework Towards Streaming Target Speaker Extraction via Chunk-wise Interleaved Splicing of Autoregressive Language Model BEAT: Tokenizing and Generating Symbolic Music by Uniform Temporal Steps Deep Supervised Contrastive Learning of Pitch Contours for Robust Pitch Accent Classification in Seoul Korean HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models UAF: A Unified Audio Front-end LLM for Full-Duplex Speech Interaction Voice of India: A Large-Scale Benchmark for Real-World Speech Recognition in India Tadabur: A Large-Scale Quran Audio Dataset
A Music-generating System Inspired by the Science of Comp...
Shawn Bell, Liane Gabora · 2016-10-08 · via cs.SD updates on arXiv.org

This paper presents NetWorks (NW), an interactive music generation system that uses a hierarchically clustered scale free network to generate music that ranges from orderly to chaotic. NW was inspired by the Honing Theory of creativity, according to which human-like creativity hinges on (1) the ability to self-organize and maintain dynamics at the 'edge of chaos' using something akin to 'psychological entropy', and (2) the capacity to shift between analytic and associative processing modes. At the 'edge of chaos', NW generates patterns that exhibit emergent complexity through coherent development at low, mid, and high levels of musical organization, and often suggests goal seeking behaviour. The architecture consists of four 16-node modules: one each for pitch, velocity, duration, and entry delay. The Core allows users to define how nodes are connected, and rules that determine when and how nodes respond to their inputs. The Mapping Layer allows users to map node output values to MIDI data that is routed to software instruments in a digital audio workstation. By shifting between bottom-up and top-down NW shifts between analytic and associative processing modes.