33 articles tagged with #audio-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Google has released Lyria 3 Pro, an upgraded AI music generation tool that can create tracks up to three minutes long, six times longer than the previous 30-second limit. The tool allows users to prompt for specific song elements like intros, choruses, and bridges, and can generate both music and lyrics from text prompts or reference photos.
Researchers have introduced LabelBuddy, an open-source audio annotation tool that uses AI assistance to bridge the gap between human intent and machine understanding in music information retrieval. The tool features collaborative tagging, containerized AI model backends, and supports multi-user consensus for creating richer audio datasets.
Researchers have developed TVF (Time-Varying Filtering), a lightweight 1 million parameter speech enhancement model that combines digital signal processing with deep learning for real-time speech denoising. The model uses a neural network to predict coefficients for a 35-band IIR filter cascade, offering interpretable processing while adapting dynamically to changing noise conditions.
CodecFlow is a new neural codec-based framework for speech bandwidth extension that efficiently reconstructs high-quality audio in compact latent space. The system uses conditional flow matching and residual vector quantization to improve speech clarity by restoring high-frequency content from low-bandwidth audio.
The article discusses real-time AI sound generation technology running on Arm processors, positioning it as a tool for creative freedom. This represents an advancement in AI-powered audio creation capabilities on mobile and edge devices.
Researchers introduce SyncTrack, an AI model for multi-track music generation that addresses rhythmic stability and synchronization issues in existing models. The model uses track-shared modules for common rhythm and track-specific modules for diverse timbres, introducing new metrics to evaluate multi-track music quality.
Researchers introduce AudioCapBench, a new benchmark for evaluating how well large multimodal AI models can generate captions for audio content across sound, music, and speech domains. The study tested 13 models from OpenAI and Google Gemini, finding that Gemini models generally outperformed OpenAI in overall captioning quality, though all models struggled most with music captioning.
The article title references 'Evaluating Audio Reasoning with Big Bench Audio' but no article body content was provided for analysis. Without the actual article content, a meaningful analysis of this AI research topic cannot be completed.
