We investigate how the realism of synthetic room impulse response (RIR) datasets affects the training of DeepFilterNet3 for single-channel speech enhancement. We compare a DNS4 image-source-method (ISM) RIR dataset with a higher-acoustic-fidelity dataset generated using hybrid wave-based and geometrical acoustics simulation. Rather than isolating individual simulation factors, we compare complete RIR generation pipelines while keeping the enhancement model unchanged. Models are evaluated on unseen measured RIRs using objective speech enhancement metrics and downstream automatic speech recognition (ASR). Training with the higher-fidelity dataset consistently yields modest improvements in objective metrics and substantially lower ASR word error rates than the ISM dataset. Although the experiments do not attribute these gains to individual modelling components, they show that increasing the overall realism of synthetic acoustic training data improves the generalization of DeepFilterNet3 to unseen measured environments.
Figures & tables
Fig. 1 : Histogram of Sabine RT60 values for Hybrid and ISM matched datasets
Fig. 2 : Level of detail for one of the rooms simulated in the Hybrid dataset
Parameter
Value
Network version
0.5.7pre
Network type
deepfilternet3
DRR
0.3
RT60target
0.05s
Late reverberation offset
5ms
preverb
1.00
Table 1: DeepFilterNet3 training configuration
Speech
Config
RIR type
Case
Small 30 ep.
Default
Hybrid
1
ISM
2
Small † 30 ep.
No decay aug.
Hybrid
3
ISM
4
Full 30 ep.
Default
Hybrid
5
ISM
6
Table 2: Overview of experimental configurations. Speech samples extracted from DNS4 English dataset with the addition of PTDB-TUG. † RT decay randomization from DFN3 disabled
Setting
Metric
ISM
Hybrid
Δ
Small 30 ep.
PESQ
2.17
2.35
+0.18
SISDRi
1.20
1.41
+0.21
STOI
0.700
0.714
+0.014
SRMR
9.80
9.97
+0.18
Small † 30 ep.
PESQ
2.18
2.39
+0.20
SISDRi
1.30
1.35
+0.05
Table 3: Objective evaluation metrics (median) for DFN3 trained with ISM and Hybrid RIR datasets across different training regimes. † RT decay randomization from DFN3 disabled.
Metric
Δ (Hybrid–ISM)
95% CI
PESQ
+0.166
[+0.149, +0.184]
SISDRi
+0.110
[+0.040, +0.182]
STOI
+0.013
[+0.010, +0.015]
SRMR
+0.270
[+0.213, +0.328]
Table 4: Overall paired improvement of Hybrid over ISM across all configurations. Values are mean differences with 95% bootstrap confidence intervals paired at utterance level.
Model
WER ↓
Δ vs noisy
Rel. imp. (%)
Clean reference
0.0224
+0.1447
–
Hybrid (Treble) training
Full 30 ep.
0.1274
+0.0397
23.8%
Full 120 ep.
0.1290
+0.0381
22.8%
Small † 30 ep.
0.1399
+0.0272
16.3%
Small 30 ep.
0.1470
+0.0201
12.0%
Table 5: ASR performance (WER) using NeMo on enhanced speech. Δ vs noisy denotes the absolute WER reduction with respect to the noisy baseline (0.1671), while Rel. imp. (%) denotes the corresponding relative improvement. Training with the Hybrid (Treble) dataset consistently yields lower WER than training with the ISM (DNS4) dataset across all evaluated configurations. † RT decay randomization from DFN3 disabled.
Speech enhancement improves intelligibility and listening quality by suppressing noise, reverberation, and competing speakers in recorded or live audio.