Audio token modeling has become a powerful framework for speech synthesis, with two-stage approaches employing semantic tokens remaining prevalent. In this paper, we aim to simplify this process by introducing a semantic knowledge distillation method that enables high-quality speech generation in a single stage. Our proposed model improves speech quality, intelligibility, and speaker similarity compared to a single-stage baseline. Although two-stage systems still lead in intelligibility, our model significantly narrows the gap while delivering comparable speech quality. These findings showcase the potential of single-stage models to achieve efficient, high-quality TTS with a more compact and streamlined architecture.

Contrastive learning has emerged as a powerful technique in audio-visual representation learning, leveraging the natural co-occurrence of audio and visual modalities in webscale video datasets. However, conventional contrastive audio-visual learning (CAV) methodologies often rely on aggregated representations derived through temporal aggregation, neglecting the intrinsic sequential nature of the data. This oversight raises concerns regarding the ability of standard approaches to capture and utilize fine-grained information within sequences. In response to this limitation, we propose sequential contrastive audiovisual learning (SCAV), which contrasts examples based on their non-aggregated representation space using multidimensional sequential distances. Audio-visual retrieval experiments with the VGGSound and Music datasets demonstrate the effectiveness of SCAV, with up to 3.5× relative improvements in recall against traditional aggregation-based contrastive learning and other previously proposed methods, which utilize more parameters and data. We also show that models trained with SCAV exhibit a significant degree of flexibility regarding the metric employed for retrieval, allowing us to use a hybrid retrieval approach that is both effective and efficient.

Recent works have shown the capability of deep generative models to tackle general audio synthesis from a single label, producing a variety of impulsive, tonal, and environmental sounds. Such models operate on band-limited signals and, as a result of an autoregressive approach, they are typically conformed by pre-trained latent encoders and/or several cascaded modules. In this work, we propose a diffusion-based generative model for general audio synthesis, named DAG, which deals with full-band signals end-to-end in the waveform domain. Results show the superiority of DAG over existing label-conditioned generators in terms of both quality and diversity. More specifically, when compared to the state of the art, the band-limited and full-band versions of DAG achieve relative improvements that go up to 40 and 65%, respectively. We believe DAG is flexible enough to accommodate different conditioning schemas while providing good quality synthesis.

This paper presents a frame-based system for estimating multiple fundamental frequencies (F0s) of polyphonic music signals based on the short-time Fourier transform (STFT) representation. To estimate the number of sources along with their F0s, it is proposed to estimate the noise level beforehand and then jointly evaluate all the possible combinations among pre-selected F0 candidates. Given a set of F0 hypotheses, their hypothetical partial sequences are derived, taking into account where partial overlap may occur. A score function is used to select the plausible sets of F0 hypotheses. To infer the best combination, hypothetical sources are progressively combined and iteratively verified. A hypothetical source is considered valid if it either explains more energy than the noise, or improves significantly the envelope smoothness once the overlapping partials are treated. The proposed system has been submitted to Music Information Retrieval Evaluation eXchange (MIREX) 2007 and 2008 contests where the accuracy has been evaluated with respect to the number of sources inferred and the precision of the F0s estimated. The encouraging results demonstrate its competitive performance among the state-of-the-art methods.

In analyzing polyphonic signals, the handling of overlapping partials is one important problem. The assumptions usually made for partial overlaps are the additivity of the linear spectrum or that of the power spectrum. In this study, the expected amplitude of two overlapping partials is derived based on the assumption that the partials overlap at the same frequency and the phase is uniformly distributed. An overlap chain rule algorithm is proposed to estimate the amplitude for the case that more than two partials overlap. The proposed algorithm has demonstrated its better accuracy over the usual two model assumptions.

The article is concerned with the estimation of fundamental frequencies, or F0s, in polyphonic music. We propose a new method for jointly evaluating multiple F0 hypotheses based on three physical principles, harmonicity, spectral smoothness and synchronous amplitude evolution, within a single source. Based on the generative quasiharmonic model, a set of hypothetical partial sequences is derived and an optimal assignment of the observed peaks to the hypothetical sources and noise is performed. The hypothetical partial sequences are then evaluated by a score function which formulates the guiding principles in a mathematical manner. The algorithm has been tested on a large collection of artificially mixed polyphonic samples and the results show the competitive performance of the proposed method.