Speech Analysis TRIZ Cases

A multichannel audio compression method identifies sound sources and determines spatial resolution based on psycho-acoustic properties.

A TuneURL app records acoustic fingerprints from audio signals to automatically save associated web links on mobile devices.

A pre-filter with a variable warping characteristic adapts to audio signals, resolving the trade-off between speech and music coding quality at low bitrates.

Virtual room simulations create training corpora that enable machine learning models to distinguish desired sounds from interfering noise and reverberations.

A voice-based digital signature service authenticates users via voice recordings and PINs.

Pre-populating the target signal buffer with extrapolated values reduces frame boundary artifacts when switching between MDCT and ACELP audio encoders.

Dynamic latency adjustment minimizes audio-video lag in Bluetooth networks by adapting buffer depth to real-time packet arrival variations.

A transform coding apparatus calculates weighted distortion to prioritize smaller scale factors.

A time scaler computes signal quality before scaling to prevent audible distortions.

A dual-stage noise reduction architecture processes audio signals using adaptive and single-channel filtering to extract desired speech from mixed acoustic inputs.

Intermediate buffering adapts audio frames between incompatible VoIP clients, resolving the trade-off between cross-device compatibility and transmission delay.

A group sound enhancement system detects collective audio events and reproduces simulated responses across all participants.

A signal processing apparatus combines outputs from multiple blind source separators to iteratively compute separated signals.

Neural network hidden layer output generates compact d-vectors for speaker identity detection.

A frequency-domain long-term predictor estimates optimal parameters using spectral flatness and vector quantization metrics.

A biomechanical system translates acoustic data into 3D vocal models for precise transcription.

Decoding apparatus generates pseudo higher frequency spectrum using lower frequency envelope and phase randomization.

A noise filler regenerates missing spectral components using decoded coefficients to preserve the temporal envelope in audio signals.

A neural network model generates latent vectors from time and frequency domain features to restore audio signals.

A noise suppression device identifies event sounds using signal processing to generate notification signals.

A self-adjusting fundamental frequency accentuation subsystem isolates and amplifies the core pitch component from a singer's voice signal.

A decoder adjusts CELP suppression per frequency band to improve audio quality.

Mobile computing device performs local voiceprint comparison to trigger an alert sound, enabling offline location without network connectivity.

A vocal training apparatus combines acoustic quality detection with biometric health monitoring to provide real-time feedback.

Spectral subtraction removes late reflection sound from single-channel speech signals using linear superposition of previous frame power spectra.

A multichannel loudspeaker calibration method modifies impulse responses by removing imperceptible reflections to determine a precise filtering matrix.

A processor determines phase shift parameters based on signal-to-noise ratios to perform adaptive beamforming.

An adaptive speech filter analyzes audio signals to attenuate ambient noise while preserving speech clarity.

Matrix decomposition reduces computational complexity by merging ipsilateral and contralateral filters for efficient spatial rendering.

Positional tracking listeners relay dynamic content changes to screen readers, resolving information loss from linear reading.

A planar microphone array detects dominant audio sources by computing discrete spatial maps across frequency sub-bands.

A spillover manager analyzes audio signals to detect media exposure.

Spectral weights redistribute noise during vector quantization of harmonic signals, preserving weak harmonics and reducing artifacts in low bitrate encoding.

A personalized machine learning model predicts user movements from audio input to generate authentic avatar animations.

Acoustic time-of-flight measurements determine microphone coordinates, replacing subjective visual alignment with precise automated positioning.

A signal normalizer adjusts low band excitation signals using calculated factors to maintain precision during fixed-point processing.

A hearing device calculates social interaction metrics using integrated microphones and classifiers to analyze audio signals.

A default greeting generator synthesizes personalized audio messages from user database records using text-to-speech conversion.

A signal processing device converts input signals to a time-frequency domain and estimates peak intensities for target and noise bands.

Segmenting audio into frequency bins enables adaptive filtering that isolates speakers while minimizing background noise in multi-speaker environments.

A voice analysis system extracts feature data to generate visual images of callers for display on call screens.

Preliminary speaker identification selects personalized models to resolve accuracy trade-offs for rare voice characteristics.

A processing system acquires voice, facial expression, and pulse wave data to calculate stress, fatigue, and mood levels.

Generative model processes decoder output using codec information to enhance signal quality, reducing artifacts at low bit-rates.

A nonlinear function generates low frequency components to extend audio signal bandwidth.

A speech enhancement system extracts embedding vectors from detected audio to identify target speakers without pre-registration.

A media gateway generates modified packets with signaling bits to indicate which audio coding stream to decode.

Sparse auditory images bridge audio and text queries, resolving mismatched feature representations.

A hearing device uses a neural network to convert signal-to-noise ratio estimates into gain values for audio processing.

The method corrects asynchronous audio signals from independent consumer devices, resolving the trade-off between measurement precision and equipment complexity.