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Acoustic word embeddings for speech search slides For a number of speech tasks, it can be useful to represent speech segments of arbitrary length by fixed-dimensional vectors, or embeddings. In particular, vectors representing word segments — acoustic word embeddings — can be used in query-by-example Badlani classes, example-based speech recognition, or spoken term discovery.
This talk will present our work on acoustic word embeddings and their application to query-by-example search.
I will speculate on applications across a wider variety of audio tasks. Her recent work includes multi-view representation learning, segmental neural models, acoustic word embeddings, and automatic sign language recognition. Yu-An Chung and James Glass. The design of the proposed model is based on the RNN Encoder-Decoder framework, and borrows the methodology of continuous skip-grams for training.
The learned vector representations are evaluated on 13 widely used word similarity benchmarks, and achieved competitive results to that of GloVe.
The biggest advantage of the proposed model is its capability of extracting semantic information of audio segments taken directly from raw speech, without relying on any other modalities such as text or images, which are challenging and expensive to collect and annotate.
Multi-Speaker Localization Using Convolutional Neural Network Trained with Noise slidesBibTeX The problem of multi-speaker localization is formulated as a multi-class multi-label classification problem, which is solved using a convolutional neural network CNN based source localization method.
Utilizing the common assumption of disjoint speaker activities, we propose a novel method to train the CNN using synthesized noise signals. The proposed localization method is evaluated for two speakers and compared to a well-known steered response power method.
Shrikant Venkataramani, Paris Smaragdis. We present an auto-encoder neural network that can act as an equivalent to short-time front-end transforms. We demonstrate the ability of the network to learn optimal, real-valued basis functions directly from the raw waveform of a signal and further show how it can be used as an adaptive front-end for end-to-end supervised source separation.
Learning and transforming sound for interactive musical applications Recent developments in object recognition especially convolutional neural networks led to a new spectacular application: But what would be the music version of style transfer? In the flow-machine project, we created diverse tools for generating audio tracks by transforming prerecorded music material.
Our artists integrated these tools in their composition process and produced some pop tracks. I present some of those tools, with audio examples, and give an operative definition of music style transfer as an optimization problem.
Such definition allows for an efficient solution which renders possible a multitude of musical applications: His mission is bridging the gap between between creative artists and intelligent technologies.
His previous academic research also includes unsupervised music generation and ensemble performance analysis, this research was carried out during my M. He has a double degree in Mathematics from Bologna University.
The objective in this work are to reduce the number of parameters in RNN and maintain their expressive power. To evaluate our proposed models, we compare it with uncompressed RNN on polyphonic sequence prediction tasks. We used the mixture signal as a condition to generate singing voices and applied the U-net style network for the stable training of the model.
Experiments with the DSD dataset show the promising results with the potential of using the GANs for music source separation. Most previous studies extract the feature vectors that characterize the cover song relation from a pair of songs and used it to compute the dis similarity between the two songs.
Based on the observation that there is a meaningful pattern between cover songs and that this can be learned, we have reformulated the cover song identification problem in a machine learning framework.
To do this, we first build the CNN using as an input a cross-similarity matrix generated from a pair of songs. We then construct the data set composed of cover song pairs and non-cover song pairs, which are used as positive and negative training samples, respectively.kaja-net.com is tracked by us since October, Over the time it has been ranked as high as in the world, while most of its traffic comes from .
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