audio-visual separation, audio & speech
Audio-visual source separation uses visual information about sound sources to guide separation of mixed audio into individual components.
9,967 technical terms and definitions
Audio-visual source separation uses visual information about sound sources to guide separation of mixed audio into individual components.
Use both audio and lip movements.
Audio-visual speech synchronization aligns acoustic features with visual lip movements for improved recognition in noisy environments.
Determine if audio and video are synced.
Audio models handle speech-to-text (ASR), text-to-speech (TTS), and voice conversation. They enable real-time voice assistants around an LLM core.
AudioLM generates natural speech and coherent audio continuations by combining semantic tokens with acoustic tokens in a hierarchical framework.
Generate coherent audio continuations using language modeling.
Third-party quality assessment.
Self-assessment of quality system.
Audit checklists guide systematic examination of requirements.
Audit findings document nonconformances or opportunities for improvement.
Log all LLM interactions for audit. Include inputs, outputs, timestamps, user IDs. Required for compliance.
Record all model accesses for accountability.
Audit schedules plan systematic reviews ensuring compliance and effectiveness.
Surface elemental analysis with depth profiling.
Three-particle recombination process.
Adversarial augmentation.
Augmentation maximizing difficulty.
Data augmentation creates synthetic examples. Flip, rotate, crop images.
Add extra dimensions for more expressive dynamics.
Auth0 provides authentication. Identity management.
Confirm content hasn't been tampered with.
Detect periodic patterns.
Automatically generate chain-of-thought examples.
Automatically adjust resources based on demand.
Auto-vectorization automatically generates SIMD code from scalar operations.
Compiler-generated vectorization.
Ensemble of attacks for robust evaluation.
Search for best augmentation policy.
AutoAugment learns augmentation policy. Automated data augmentation.
Autoclave testing applies steam pressure simulating extended humidity exposure.
High pressure steam aging.
High-pressure steam aging test.
Measure small angles precisely.
Handle time-series dependencies.
Autocorrelation functions measure correlation between time series observations at different lags.
Autoencoder-based forecasting learns compressed representations of time series for prediction.
Autoencoders detect anomalies through reconstruction error with normal patterns reconstructed better than anomalies.
Autoformer for time series decomposes trends and seasonal components using auto-correlation mechanism instead of point-wise attention.
Autoformer applies NAS to vision transformer design discovering efficient attention patterns and layer configurations.
AutoGen facilitates multi-agent conversations for complex task solving.
AutoGen and CrewAI orchestrate multiple agents that collaborate, debate, or specialize on subtasks.
AutoGPT demonstrates autonomous agent capabilities through iterative goal pursuit.
Autonomous agent that breaks goals into tasks.
Autograd automatically computes gradients. Backward pass through computation graph. Foundation of training.
Automatic Feature Interaction learning uses multi-head self-attention to model high-order feature combinations.
Map crystal orientations in TEM.
Fix bugs automatically.
ML-based defect categorization.
Use AI to verify facts at scale.