Noise Suppression in Speech Signals using Artificial Intelligence

---

Zıvalıoğlu C. C., Akay O.

INTERNATIONAL CONFERENCE on ELECTRICAL and ELECTRONICS ENGINEERING (ELECO 2025), Bursa, Türkiye, 27 - 29 Kasım 2025, ss.1-5, (Tam Metin Bildiri)

• Yayın Türü: Bildiri / Tam Metin Bildiri
• Basıldığı Şehir: Bursa
• Basıldığı Ülke: Türkiye
• Sayfa Sayıları: ss.1-5
• Dokuz Eylül Üniversitesi Adresli: Evet

Özet

This study presents a hybrid approach combining deep learning-based noise classification and subspace projection for environmental noise suppression in speech signals. Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a CNN-LSTM hybrid model are employed for noise type classification. Prediction smoothing is applied to improve robustness in silent and lowSNR regions of speech. The proposed system achieves noticeable improvements in terms of signal-to-noise ratio (SNR), short-time objective intelligibility (STOI), and perceptual evaluation of speech quality (PESQ) under stationary and nonstationary noise scenarios.