Speech Analysis TRIZ Cases

Frequency folding expands narrowband audio signals using shelf filters to resolve the trade-off between high audio quality and low computational complexity.

A signal processing apparatus detects voice presence to correct mixed signals before shaping the output.

Audio processing system removes involuntary speech artifacts like stuttering and lisps to deliver smooth communication signals.

Feedforward earphones extract specific frequency signals alongside noise reduction to retain valuable environmental audio like alarms and voices.

A speech classifier generates binary scores from raw audio data to create precise timing windows for caption text insertion.

An adaptive filter determines an update order range based on cross-correlation between left and right audio channels to reduce computational load.

K-means clustering selects prediction models to reconstruct high-frequency content, improving precision without side information.

A speech error concealment method generates substitute frames using linear prediction analysis and synthesis filters to maintain continuous audio output.

A system detects audio and visual cues to identify objects of interest in augmented reality environments.

A speech signal processing apparatus adjusts envelope levels using threshold detection and edge control to manage audio dynamics.

A null-steered beamformer separates direct and reverberant energy to estimate the Direct-to-Reverberant Ratio.

Combining pitch values from both ears improves detection precision in low frequencies, resolving poor speech intelligibility in tone languages.

Allocating media content segments enables automatic identifier exchange via watermarks, eliminating manual data entry errors and complex typing.

Segmenting speech encoding modes into groups with shared parameters lowers processing complexity while maintaining network capacity and signal quality.

Time-variable side information enables accurate audio object reconstruction while reducing computational complexity during decoding.

A voice processing system tracks multiple speakers by extracting position data from microphone arrays and updating a speaker registry.

A meter selects audio signature schemes based on detected environmental noise and content frequency to generate monitored signatures.

A system generates synthetic acoustic impulse responses by modifying direct-to-reverberant ratio and reverberation time parameters.

Correlation factors mediate packet recovery across channels, resolving the trade-off between wireless convenience and audio reliability.

A multi-mode audio decoder applies spectral shaping to decoded coefficients across linear-prediction and frequency-domain modes.

A signal enhancer merges multiple filtered audio signals using weighted sums based on speech probability to improve intelligibility.

Segmenting compressed HOA signals into independent base and enhancement layers enables scalable audio delivery.

A two-stage classification system distinguishes user-generated noise from professional audio using machine learning models.

Segmenting speech signals into frequency bins enables independent magnitude and phase processing for targeted noise suppression.

Individual trajectory segment length adjustment reduces audio encoding bit requirements while maintaining decoded signal quality.

A digital audio processing system partitions signals into segments and compares spectral components to identify and modify noise anomalies.

A spatial audio capture system generates descriptive metadata for encoder input format data to support real-time streaming sessions.

An emotion regulation system calculates utterance scores to trigger constructive conversation interventions.

A decoder synthesizes side signals using inter-channel prediction gain parameters to reconstruct audio output.

A linear predictive coding decoder reconstructs lost speech frames by extrapolating excitation samples from previously received good frames.

Segmenting renderer configuration into distinct packet types reduces decoding complexity while maintaining high-definition spatial audio quality.

Frequency segmentation separates shock waves from reflections, enabling precise weapon identification and round counting without room-specific calibration.

An external subwoofer reproduces low-frequency sounds synchronized with a model train's motion, overcoming the limited bass output of internal compact speakers.

Encoder divides audio signals into frequency sub-bands to classify speech-like and music-like components for targeted excitation selection.

An imaging device multiplexes moving image coded data with audio coded data using a control unit to manage reproduction position.

A feature extractor generates latent-space representations of time-series data segments for downstream processing.

A deep neural network processes binaural audio signals to generate rotated output matching head orientation.

A video playback system segments content into scenes based on subtitle timing to enable targeted language learning.

A semi-supervised learning device combines recurrent neural networks with variational autoencoders to infer human emotions from observational data.

A detection system combines microphone audio data with gyroscope mechanical oscillation measurements to identify ultrasonic signals in user devices.

N-N/2-N structure generates multi-channel output signals from downmix inputs using matrix operations and decorrelators.

Encoding apparatus adapts high frequency signal processing via low band excitation spectra, resolving bit allocation trade-offs that degrade sound quality.

Processor detects microphone positions to select appropriate audio processing algorithms for electronic devices.

Encoding a unique signature in the infrasound range preserves creator identification through YouTube compression, bypassing format-specific DRM limitations.

Encoding apparatus reduces processing samples for wideband signals using orthogonal transforms and integration.