Speech Analysis TRIZ Cases

A method generates audio fingerprint hashes by detecting significant peaks within spectrogram windows to create unique identifiers for media streams.

A correction unit standardizes echo signal features extracted from the ear to a reference device profile for consistent biometric verification.

Trainable front ends and back ends adapt to evolving voice synthesis attacks without relying on fixed feature assumptions.

A signal processing device analyzes frequency component importance to tailor noise suppression strength for each band.

Audio decoding device separates channel-based and object-based signals to reduce computational load while maintaining high audio quality.

A mobile handset visual indicator detects discontinuous transmission periods to signal remote party silence.

Embedded digital watermarks enable automatic identification of aired programming, eliminating manual intervention errors in post-air compliance logs.

A decoder determines stability values from adjacent spectral envelope differences to select appropriate decoding modes for audio frames.

Signal processing method suppresses impact sound by maintaining phase continuity through intermediary phase data.

Adaptive gain smoothing resolves pumping artifacts by dynamically switching time constants based on signal state, preserving speech intelligibility.

Segmented audio classification selects adaptive embedding parameters to balance perceptual quality against detection reliability in noisy environments.

Embedding sparseness and sign symmetry data in bitstreams allows playback devices to reproduce intended sound fields accurately regardless of speaker geometry.

Server mediation manages multi-user chorus connections, reducing network latency while maintaining precise audio synchronization.

Audio encoding device corrects gain information using differential prediction from past frames, reducing code rate while maintaining per-band sound quality.

Serial adaptive notch filters suppress non-percussive audio components, enabling high-accuracy separation without fundamental frequency estimation.

A terrestrial trunked radio gateway uses a PCM driver to insert ACELP voice data for transmission.

Phonetic encoding enables encrypted search of speech data, resolving the trade-off between computational efficiency and data security.

A speech enhancement method adjusts spectral subtraction parameters based on predicted power spectra to improve signal quality.

Active learning filters environmental data through single-modal models to select key samples, reducing manual labeling time while improving detection precision.

Acoustical signature detection classifies earbud position using user-specific machine learning models for precise in-ear status identification.

A joint deep neural network processes audio signals using a two-stage training approach with weak labels.

Autoregressive modeling separates noise and reverberation estimation, maintaining audio quality while reducing computational complexity.

Modified framing rules minimize data overhead and artifacts during mode transitions, maintaining high coding efficiency.

Optimized training sequence computation updates selected Gaussian Mixture Model components to reduce processor power and memory usage in speaker recognition.

Audio encoding system generates downmix signals and spatial metadata for multi-channel upmixing.

Adaptive least mean squares filter processes multichannel audio signals in the frequency domain to reduce reverberation artifacts.

Dynamic gain adjustment based on frequency band uncertainty values balances aggressive noise suppression against speech distortion in microphone outputs.

A spatialized audio system detects listener head pose to render accurate sound fields for virtual sources.

Audio processing apparatus detects voice sections using spatial spectrum power analysis to exclude non-voice content that degrades recognition accuracy.

Encoder aligns multi-microphone signals via temporal compensation and spectral mapping to resolve phase mismatches that degrade coding efficiency.

Automated analysis replaces human examiners to eliminate fatigue and bias, ensuring consistent stress scoring accuracy.

Beamforming localizes acoustic signals from livestock animals using multiple microphones for precise health monitoring.

Bundling silence indicator frames reduces uplink signaling overhead and latency while preserving spectral efficiency in VoLTE and VoNR networks.

System corrects transcription errors and attributes speech tags to participants, resolving summarization accuracy issues in heterogeneous transcripts.

Auxiliary function approximation reduces computational burden in blind sound source separation, enabling real-time adaptation to environmental changes.

A non-speech audio processing system compares vocalizations against prerecorded files using edit distance metrics to assign mimic quality values.

A joint de-noise and de-reverberation model processes audio signals using guided training with auxiliary teacher models to generate cleaned output.

A neural network separates target audio signals by steering a virtual microphone direction toward the selected speaker.

Identifying transient blocks and arranging their spectra improves encoding quality for compressed audio signals.

A multi-channel audio encoder switches between parametric and individual channel modes to adapt encoding strategies.

Biomimetic encoding preserves semantic order in compressed streams, eliminating reconstruction needs that degrade quality.

Adjusts HOA soundfields using screen and viewing window parameters to align acoustic elements with visual content.

DCT sign-only correlation approximates audio similarity without spectral sensitivity, resolving low matching rates in distorted signals.

A sound source probing apparatus determines acoustic direction by learning weights to combine pre-calculated correlation matrices.

Segmenting higher order ambisonic audio allows distinct renderers to minimize error and prevent artifacts during playback.

Detecting spatial extension data types enables consistent decoding of core audio and residual coding data.

A spectral compressor maps high frequency speech components to lower bands, preserving signal quality within limited bandwidths.