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

M
MIT News - Artificial intelligence
罗磊的独立博客
Hugging Face - Blog
Hugging Face - Blog
Apple Machine Learning Research
Apple Machine Learning Research
Last Week in AI
Last Week in AI
S
SegmentFault 最新的问题
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
美团技术团队
人人都是产品经理
人人都是产品经理
WordPress大学
WordPress大学
The Cloudflare Blog
IT之家
IT之家
雷峰网
雷峰网
小众软件
小众软件
博客园 - 叶小钗
博客园 - 聂微东
爱范儿
爱范儿
博客园 - 司徒正美
博客园 - 三生石上(FineUI控件)
V
Visual Studio Blog
博客园 - 【当耐特】
V
V2EX
博客园_首页
T
Tailwind CSS Blog

eess.AS updates on arXiv.org

Dependence on Early and Late Reverberation of Single-Channel Speaker Distance Estimation MIST: Multimodal Interactive Speech-based Tool-calling Conversational Assistants for Smart Homes LiVeAction: a Lightweight, Versatile, and Asymmetric Neural Codec Design for Real-time Operation Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization PairAlign: A Framework for Sequence Tokenization via Self-Alignment with Applications to Audio Tokenization WavCube: Unifying Speech Representation for Understanding and Generation via Semantic-Acoustic Joint Modeling Predictive-Generative Drift Decomposition for Speech Enhancement and Separation Minimizing Modality Gap from the Input Side: Your Speech LLM Can Be a Prosody-Aware Text LLM X-Voice: Enabling Everyone to Speak 30 Languages via Zero-Shot Cross-Lingual Voice Cloning JASTIN: Aligning LLMs for Zero-Shot Audio and Speech Evaluation via Natural Language Instructions Phoneme-Level Deepfake Detection Across Emotional Conditions Using Self-Supervised Embeddings When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition Dimensionality-Aware Anomaly Detection in Learned Representations of Self-Supervised Speech Models Mitigating Multimodal LLMs Hallucinations via Relevance Propagation at Inference Time Virtual Speech Therapist: A Clinician-in-the-Loop AI Speech Therapy Agent for Personalized and Supervised Therapy LASE: Language-Adversarial Speaker Encoding for Indic Cross-Script Identity Preservation Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe MMAudioReverbs: Video-Guided Acoustic Modeling for Dereverberation and Room Impulse Response Estimation Alethia: A Foundational Encoder for Voice Deepfakes From Birdsong to Rumbles: Classifying Elephant Calls with Out-of-Species Embeddings Beyond the Baseband: Adaptive Multi-Band Encoding for Full-Spectrum Bioacoustics Classification Predicting Upcoming Stuttering Events from Three-Second Audio: Stratified Evaluation Reveals Severity-Selective Precursors, and the Model Deploys Fully On-Device The False Resonance: A Critical Examination of Emotion Embedding Similarity for Speech Generation Evaluation DiffAnon: Diffusion-based Prosody Control for Voice Anonymization Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech Similarity Choice and Negative Scaling in Supervised Contrastive Learning for Deepfake Audio Detection Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models Praxy Voice: Voice-Prompt Recovery + BUPS for Commercial-Class Indic TTS from a Frozen Non-Indic Base at Zero Commercial-Training-Data Cost
PocketVAE: A Two-step Model for Groove Generation and Con...
Kyungyun Lee, Wonil Kim, Juhan Nam · 2021-07-11 · via eess.AS updates on arXiv.org

Creating a good drum track to imitate a skilled performer in digital audio workstations (DAWs) can be a time-consuming process, especially for those unfamiliar with drums. In this work, we introduce PocketVAE, a groove generation system that applies grooves to users' rudimentary MIDI tracks, i.e, templates. Grooves can be either transferred from a reference track, generated randomly or with conditions, such as genres. Our system, consisting of different modules for each groove component, takes a two-step approach that is analogous to a music creation process. First, the note module updates the user template through addition and deletion of notes; Second, the velocity and microtiming modules add details to this generated note score. In order to model the drum notes, we apply a discrete latent representation method via Vector Quantized Variational Autoencoder (VQ-VAE), as drum notes have a discrete property, unlike velocity and microtiming values. We show that our two-step approach and the usage of a discrete encoding space improves the learning of the original data distribution. Additionally, we discuss the benefit of incorporating control elements - genre, velocity and microtiming patterns - into the model.