Time-series signal AI: STT, TTS, and anomaly detection
Turn sound and signal data into the language of AI
Audio & signal AI development is an R&D service that builds speech recognition (STT), speech synthesis (TTS), acoustic/vibration/sensor signal analysis, and anomaly detection systems using state-of-the-art models such as Whisper, wav2vec2, and VITS. TreeSoop’s POSTECH-trained signal processing and speech AI engineers deliver 95%+ Korean STT accuracy and sub-1ms real-time inference, with deployments across call centers, voice assistants, and smart factories — rated 4.92/5 on Wishket, Korea’s largest dev-outsourcing platform (top 0.1% partner).
Production lines stop without warning because equipment failures cannot be predicted in advance, while preventive-maintenance spending keeps running over budget.
Signals captured in high-noise environments — factories, vehicles, outdoor settings — are low quality, and conventional analysis methods lose much of their accuracy.
Hiring specialists who understand both classical signal processing theory (FFT, wavelet transforms) and deep learning is extremely difficult.
We analyze vibration, temperature, and current sensor data in real time to predict the remaining useful life of critical components — bearings, motors, pumps — and recommend the optimal maintenance window.
Spectral Subtraction and Deep Noise Suppression (DNS) models dramatically improve SNR, enabling reliable signal analysis even in noisy environments.
Autoencoder and LSTM models trained on normal operating patterns detect early warning signs in sensor data and send alerts before failures occur.
We separate individual speakers in multi-speaker audio and run per-speaker STT — powering automated meeting transcripts, call-center quality analysis, and more.
We assess sensor types, sampling rates, noise conditions, and your analysis goals.
We design the right feature-extraction approach: FFT, STFT, mel spectrograms, and more.
We train time-series-specialized models — 1D CNN, LSTM, Transformer — and validate their performance.
We build edge- or cloud-based real-time inference with an alerting pipeline.
We pilot the system in your real operating environment, iterating on threshold tuning, alert logic, and false-positive reduction.
Analyzed vibration signals from power-generation turbines to predict bearing damage and imbalance 3 weeks in advance
An NVH inspection system that analyzes driving noise after assembly to automatically diagnose faulty components
Deep-learning analysis of stethoscope audio to flag suspected cardiac conditions such as arrhythmia and valve disorders
95%
Anomaly detection accuracy
Validated in industrial field deployments
3 weeks
Advance failure prediction
Average prediction lead time
40dB
SNR improvement
Signal quality after noise suppression
1ms
Real-time latency
Inference latency on edge devices
Our experts will recommend the best approach for your project.
