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Numerous advanced and lightweight signal processing methods have been presented for single-channel speech enhancement (SE). It is imperative to carefully explore how to efficiently combine, integrate, and balance these methods. This paper proposes a more effective and less resource-intensive SE system, focused on the integration and adaptation of several approaches, especially the temporal cepstrum smoothing (TCS). First, a more robust fundamental frequency estimator is employed within TCS, mitigating the performance limitations caused by the inaccuracy of the original estimator. Additionally, a harmonic enhancement mechanism is introduced, effectively recovering the weak harmonic components. By incorporation of the modified TCS in the a posteriori speech presence probability estimation, the unbiased minimum mean square error noise power spectral density estimator can be refined. The modified TCS is also utilized for the a priori signal-to-noise ratio estimation. Moreover, this paper enhances the log-spectral amplitude estimator by applying both super-Gaussian speech priors and speech presence uncertainty for further improvement. Experimental evaluations demonstrate that the proposed method yields an improvement in speech quality while maintaining modest computational and storage requirements. Furthermore, the proposed system exhibits comparable performance to several baseline systems based on lightweight deep neural networks. [ABSTRACT FROM AUTHOR]

This paper presents a free-field method for inverse estimation of acoustic porous material parameters from sound pressure measurements above small rectangular samples. The finite sample effect, the spherical propagation of the sound field, and a potential lateral material reaction are considered. Using an extensive series of systematically varied finite element simulations, neural network models are developed to replace computationally expensive simulations as a forward model for the calculation of the complex sound pressure above small samples in the inverse optimization. The method is experimentally validated using various porous material samples. The results show that the influence of the finite sample size is successfully removed and thus, the acoustic properties of the materials can be estimated from the determined porous parameters with high accuracy, even based on a single sound pressure measurement over small samples with pronounced edge diffraction. The poroacoustic parameters hence derived can be used directly, e.g., in simulation applications, or to calculate complex surface impedances or absorption coefficients. [ABSTRACT FROM AUTHOR]