# Speech-Signal Separation System (Advisor: Assoc. Prof. Güray Sonugür)

> My Mechatronics Engineering graduation project: a C++ pipeline combining DSP/FFT speech enhancement, neural-network-based separation and MFCC/GMM speaker-recognition evaluation.

- Author: Muhammet Ali Köker
- Language: en
- Canonical: https://alikoker.com.tr/en/speech-signal-separation-system
- Translation: https://alikoker.com.tr/ses-filtresi
- Published: 2021-06-24T18:57:28+03:00
- Modified: 2026-08-29T18:07:00+03:00
- Type: project

This was my Mechatronics Engineering graduation project at Afyon Kocatepe University: a **DSP, FFT and Machine-Learning-Based Speech Separation and Filtering System**.

The engineering goal was not simply to make noisy recordings sound cleaner to a listener. The processing had to reduce unwanted components while preserving information useful to the downstream speaker-recognition stage.

## DSP and FFT Filtering

The first layer used digital signal-processing methods and the [Fast Fourier Transform](/en/wiki/fast-fourier-transform) to inspect and filter audio in the time/frequency domain.

Frequency-domain analysis made it possible to observe how speech and environmental noise occupied the spectrum in the tested examples and to apply filtering decisions accordingly.

A key boundary was avoiding excessive suppression of speaker-related spectral information. Audio that sounds subjectively cleaner is not automatically a better biometric feature source.

## Neural-Network Separation

The second layer introduced an artificial-neural-network approach to separating speech from environmental sound. This moved part of the decision process from fixed filtering rules toward behavior learned from data.

The implementation belongs to the neural-network methods and C++ environment used at the time; the models and runtimes I used in later years were not part of this project.

## C++ Implementation and Real-Time Constraint

The software was implemented in C++ with an object-oriented structure. Acquisition/input, transforms, filtering, separation and evaluation were treated as distinguishable stages.

The real-time requirement meant that correctness alone was not enough: processing also had to keep up with the audio stream.

## MFCC/GMM Speaker Recognition

To evaluate the effect beyond waveform inspection, I implemented an [MFCC](/en/speech-feature-extraction-with-mfcc)- and GMM-based speaker-recognition stage.

The resulting pipeline can be summarized as:

```text
audio -> enhancement/separation -> MFCC -> GMM -> speaker decision
```

This made it possible to compare recognition behavior with and without the preprocessing stage.

## VoxCeleb Evaluation

The historical project record states that the system was evaluated with VoxCeleb speech data and that noisy recordings showed improved speaker-recognition behavior without observed loss on clean examples.

The surviving project record establishes that filtered and unfiltered speaker-recognition pipelines were compared on VoxCeleb speech data. Because the complete split, noise model and numerical metric table are unavailable, the supported result is limited to that comparison.

## Connection to Later Work

The project became an early engineering basis for my later speech, speaker-recognition and real-time audio systems. Later systems use different models and runtimes and treat [VAD](/en/wiki/voice-activity-detection), [speaker embeddings](/en/wiki/speaker-embedding), segmentation and capacity engineering as separate system concerns.

The graduation project was supervised by Assoc. Prof. Güray Sonugür.

## Cite This Work

Köker, M. A. (2021). Speech-Signal Separation System (Advisor: Assoc. Prof. Güray Sonugür). alikoker.com.tr. https://alikoker.com.tr/en/speech-signal-separation-system

- BibTeX: https://alikoker.com.tr/en/speech-signal-separation-system.bib
- RIS: https://alikoker.com.tr/en/speech-signal-separation-system.ris
- CSL-JSON: https://alikoker.com.tr/en/speech-signal-separation-system.csl.json