Despite the advancements in cutting-edge technologies, audio signal processing continues to pose challenges and lacks the precision of a human speech processing system. To address these challenges, we propose a novel approach to simplify audio signal processing by leveraging time-domain techniques and reservoir computing. Through our research, we have developed a real-time audio signal processing system by simplifying audio signal processing through the utilization of reservoir computers, which are significantly easier to train. Feature extraction is a fundamental step in speech signal processing, with Mel Frequency Cepstral Coefficients (MFCCs) being a dominant choice due to their perceptual relevance to human hearing. However, conventional MFCC extraction relies on computationally intensive time-frequency transformations, limiting efficiency in real-time applications. To address this, we propose a novel approach that leverages reservoir computing to streamline MFCC extraction. By replacing traditional frequency-domain conversions with convolution operations, we eliminate the need for complex transformations while maintaining feature discriminability. We present an end-to-end audio processing framework that integrates this method, demonstrating its potential for efficient and real-time speech analysis. Our results contribute to the advancement of energy-efficient audio processing technologies, enabling seamless deployment in embedded systems and voice-driven applications. This work bridges the gap between biologically inspired feature extraction and modern neuromorphic computing, offering a scalable solution for next-generation speech recognition systems.
Figures & tables
Fig. 1: Topology of Reservoir computer
Fig. 2: MFC extraction
Fig. 3: Mel filter bank
Fig. 4: A comparison between the conventional approach (a) where the reservoir is used as a classifier after complex pre-processing by different means and the approach proposed here (b) where RC is used as an end-to-end audio processing concept.
Fig. 5: Time-domain filterbank
parameter=
0.002454697
0.004909393
0.00736409
0.009818787
Δf =
131
141
151
161
parameter=
0.007781114
0.00561907
0.003457026
0.001294982
Δf =
171
181
191
201
TABLE I: Parameters and frequencies for 10 Mel frequencies used
Fig. 6: Time-domain MFCC extraction
Experiment
Reservoir
Training and Testing Function
Experiment 1 (Exp1)
2
Matlab MFCC
Experiment 2 (Exp2)
1, 2
Time domain MFCC
TABLE II: Experiments
Models
Accuracy (%)
LSM [ 24 ]
94.0
Liquid-SNN [ 20 ]
77.7
Reservoir Computing (MEMS) [ 6 ]
78
Reservoir-based(Our method(EXp 1))
92.9
Reservoir-based(Our method(EXp 2))
91.82
TABLE III: Comparison of Performance of models with Ti-46 dataset for digit recognition
Models
Accuracy (%)
CNN [ 19 ]
96.4
LSTM [ 19 ]
95.23
AudioNet(Deep-NN) [ 4 ]
92.53
Liquid-SNN [ 20 ]
82.65
Reservoir-based(Our method(EXp 1))
93.08
Reservoir-based(Our method(EXp 2))
84.81
TABLE IV: Comparison of Performance of models with Audio-Mnist dataset for digit recognition.
Fig. 7: Speaker Recognition performance of our system
Fig. 8: Digit Recognition performance of our system