Bootstrapping meaning through listening: Unsupervised learning of spoken sentence embeddings

Inducing semantic representations directly from speech signals is a highly challenging task but has many useful applications in speech mining and spoken language understanding. This study tackles the unsupervised learning of semantic representations for spoken utterances. Through converting speech signals into hidden units generated from acoustic unit discovery, we propose WavEmbed, a multimodal sequential autoencoder that predicts hidden units from a dense representation of speech. Secondly, we also propose S-HuBERT to induce meaning through knowledge distillation, in which a sentence embedding model is first trained on hidden units and passes its knowledge to a speech encoder through contrastive learning. The best performing model achieves a moderate correlation (0.5~0.6) with human judgments, without relying on any labels or transcriptions. Furthermore, these models can also be easily extended to leverage textual transcriptions of speech to learn much better speech embeddings that are strongly correlated with human annotations. Our proposed methods are applicable to the development of purely data-driven systems for speech mining, indexing and search

Progressive distillation diffusion for raw music generation

This paper aims to apply a new deep learning approach to the task of generating raw audio files. It is based on diffusion models, a recent type of deep generative model. This new type of method has recently shown outstanding results with image generation. A lot of focus has been given to those models by the computer vision community. On the other hand, really few have been given for other types of applications such as music generation in waveform domain. In this paper the model for unconditional generating applied to music is implemented: Progressive distillation diffusion with 1D U-Net. Then, a comparison of different parameters of diffusion and their value in a full result is presented. One big advantage of the methods implemented through this work is the fact that the model is able to deal with progressing audio processing and generating , using transformation from 1-channel 128 x 384 to 3-channel 128 x 128 mel-spectrograms and looped generation. The empirical comparisons are realized across different self-collected datasets.Comment: 9 page

Metastatic renal cell carcinoma from a native kidney of a renal transplant patient diagnosed by endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) biopsy

Endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) biopsy sampling of enlarged lymph nodes is increasingly used to diagnose metastatic tumors, especially of the gastrointestinal tract and the lungs. Herein, we describe the diagnosis of metastatic renal cell carcinoma from a native kidney of a 54 year-old male patient, who had a 5-years history of renal transplant, by EUS-FNA of mediastinal and celiac lymph nodes. Histological and immunohistochemical findings confirmed the origin of metastatic tumor. EUS-FNA with proper cytological evaluation can be useful in the diagnosis of metastatic renal cell carcinoma in renal transplant patients.

Comparing TorX, Autolink, TGV and UIO test algorithms

This paper presents a comparison of four algorithms for test derivation: TorX, TGV, Autolink and UIO algorithms. The algorithms are classified according to the detection power of their conformance relations. Because Autolink does not have an explicit conformance relation, a conformance relation is reconstructed for it. The experimental results obtained by applying TorX, Autolink, UIO and TGV to the Conference Protocol case study are consistent with the theoretical results of this paper