Automatic speech quality assessment aims to quantify subjective human perception of speech through computational models to reduce the need for labor-consuming manual evaluations. While models based on deep learning have achieved progress in predicting mean opinion scores (MOS) to assess synthetic speech, the neglect of fundamental auditory perception mechanisms limits consistency with human judgments. To address this issue, we propose an auditory perception guided-MOS prediction model (APG-MOS) that synergistically integrates auditory modeling with semantic analysis to enhance consistency with human judgments. Specifically, we first design a perceptual module, grounded in biological auditory mechanisms, to simulate cochlear functions, which encodes acoustic signals into biologically aligned electrochemical representations. Secondly, we propose a residual vector quantization (RVQ)-based semantic distortion modeling method to quantify the degradation of speech quality at the semantic level. Finally, we design a residual cross-attention module, coupled with a progressive learning strategy, to enable multimodal fusion of encoded electrochemical signals and semantic representations. Experiments demonstrate that APG-MOS achieves superior performance on two primary benchmarks. The implementation code is available at https://github.com/BNU-ERC-ITEA/APG-MOS.

In this work, we address the long-standing problem of automatic recorded music denoising. In previous audio denoising research, the primary focus has been on speech, and music denoising works only considered noise types in indoor conversation scenarios or old gramophone recordings, neglecting the amateur music recording scenario. To this end, we first propose MusicECAN, an automatic music denoising method designed to filter out additional noise components in recorded music. The novel architecture comprises two key components, namely, a feature learning module and a noise filtering module, which can efficiently but effectively model, refine and denoise the noisy input. Specifically, in order to capture sufficient noisy music information, an ECA-U-SAM based feature learning module is designed by incorporating an efficient channel attention (ECA) mechanism into the traditional U-Net model with a supervised attention module (SAM). To train our MusicECAN, we collect M&N, a dataset containing various clean music and noise recordings. Through the combination of different clean-noise recording pairs, we can effectively simulate possible music performance environments with various background noise. Extensive quantitative and qualitative comparisons demonstrate that our MusicECAN outperforms the state-of-the-art audio denoising methods.

Audio-to-score alignment is a multi-modal task that aims at generating an accurate mapping between symbolic and signal-level representations of musical signals, which is important for music performance analysis and retrieval. Among numerous music genres, orchestral music is a category of music with complex performance characteristics such as multi-instrument, non-percussive instrument and music expressiveness. However, previous methods do not take sufficient account of the performance characteristics of orchestral music, leading to limitations in alignment accuracy on orchestral music of these methods. To solve this problem, we present a performance quantification guided audio-to-score alignment (PQG-A2SA) method with high alignment accuracy for orchestral music at note-level. Specially, the PQG-A2SA contains two parts, namely an Inter Onset Interval (IOI) guided conditionally-constrained Dynamic Time Wrapping (DTW) and an articulation guided onset and offset detection. Different from the previous work, the IOI-guided conditionally-constrained DTW is designed to achieve a preliminary mapping between symbolic and chord-level representations of musical signals. In the second module, the onset and offset detection model under different musical articulations are established, thus refining the alignment results. We provide extensive experimental validation and analysis of our method. Our PQG-A2SA method can improve 9.0% in onset align rate and 17.5% in offset align rate at most compared with the state-of-the-art methods.