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PopMAG: Pop Music Accompaniment Generation 20

PopMAG: Pop Music Accompaniment Generation

引用

28th ACM International Conference on Multimedia (MM)

作者： Ren, Yi He, Jinzheng Tan, Xu Qin, Tao Zhao, Zhou Liu, Tie-Yan Zhejiang Univ Hangzhou Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781450379885

In pop music, accompaniments are usually played by multiple instruments (tracks) such as drum, bass, string and guitar, and can make a song more expressive and contagious by arranging together with its melody. Previous works usually generate multiple tracks separately and the music notes from different tracks not explicitly depend on each other, which hurts the harmony modeling. To improve harmony, in this paper(1), we propose a novel MUlti-track MIDI representation (MuMIDI), which enables simultaneous multi-track generation in a single sequence and explicitly models the dependency of the notes from different tracks. While this greatly improves harmony, unfortunately, it enlarges the sequence length and brings the new challenge of long-term music modeling. We further introduce two new techniques to address this challenge: 1) We model multiple note attributes (e.g., pitch, duration, velocity) of a musical note in one step instead of multiple steps, which can shorten the length of a MuMIDI sequence. 2) We introduce extra long-context as memory to capture long-term dependency in music. We call our system for pop music accompaniment generation as PopMAG. We evaluate PopMAG on multiple datasets (LMD, FreeMidi and CPMD, a private dataset of Chinese pop songs) with both subjective and objective metrics. The results demonstrate the effectiveness of PopMAG for multi-track harmony modeling and long-term context modeling. Specifically, PopMAG wins 42%/38%/40% votes when comparing with ground truth musical pieces on LMD, FreeMidi and CPMD datasets respectively and largely outperforms other state-of-the-art music accompaniment generation models and multi-track MIDI representations in terms of subjective and objective metrics.

关键词： music generation pop music accompaniment generation music representation sequence-to-sequence model

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Patching as Translation: the Data and the Metaphor 35

Patching as Translation: the Data and the Metaphor

引用

35th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Ding, Yangruibo Ray, Baishakhi Devanbu, Premkumar Hellendoorn, Vincent J. Columbia Univ New York NY 10027 USA Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9781450367684

Machine Learning models from other fields, like Computational Linguistics, have been transplanted to Software Engineering tasks, often quite successfully. Yet a transplanted model's initial success at a given task does not necessarily mean it is well-suited for the task. In this work, we examine a common example of this phenomenon: the conceit that "software patching is like language translation". We demonstrate empirically that there are subtle, but critical distinctions between sequence-to-sequence models and translation model: while program repair benefits greatly from the former, general modeling architecture, it actually suffers from design decisions built into the latter, both in terms of translation accuracy and diversity. Given these findings, we demonstrate how a more principled approach to model design, based on our empirical findings and general knowledge of software development, can lead to better solutions. Our findings also lend strong support to the recent trend towards synthesizing edits of code conditional on the buggy context, to repair bugs. We implement such models ourselves as "proof-of-concept" tools and empirically confirm that they behave in a fundamentally different, more effective way than the studied translation-based architectures. Overall, our results demonstrate the merit of studying the intricacies of machine learned models in software engineering: not only can this help elucidate potential issues that may be overshadowed by increases in accuracy;it can also help innovate on these models to raise the state-of-the-art further. We will publicly release our replication data and materials at https://***/ARiSE- Lab/Patch-as-translation.

关键词： neural machine translation big code sequence-to-sequence model automated program repair

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Parrotron: An End-to-End Speech-to-Speech Conversion model and its Applications to Hearing-Impaired Speech and Speech Separation 20

Parrotron: An End-to-End Speech-to-Speech Conversion Model a...

引用

Interspeech Conference

作者： Biadsy, Fadi Weiss, Ron J. Moreno, Pedro J. Kanvesky, Dimitri Jia, Ye Google Mountain View CA 94043 USA

We describe Parrotron, an end-to-end-trained speech-to-speech conversion model that maps an input spectrogram directly to another spectrogram, without utilizing any intermediate discrete representation. The network is composed of an encoder, spectrogram and phoneme decoders, followed by a vocoder to synthesize a time-domain waveform. We demonstrate that this model can be trained to normalize speech from any speaker regardless of accent, prosody, and background noise, into the voice of a single canonical target speaker with a fixed accent and consistent articulation and prosody. We further show that this normalization model can be adapted to normalize highly atypical speech from a deaf speaker, resulting in significant improvements in intelligibility and naturalness, measured via a speech recognizer and listening tests. Finally, demonstrating the utility of this model on other speech tasks, we show that the same model architecture can be trained to perform a speech separation task.

关键词： speech normalization voice conversion atypical speech speech synthesis sequence-to-sequence model

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Exaggerated Portrait Caricatures Generation Based On Seq2Seq 9

Exaggerated Portrait Caricatures Generation Based On Seq2Seq

引用

9th International Conference on Information Science and Technology (ICIST)

作者： Xu, Kun Tang, Chenwei Lv, Jiancheng He, Zhenan Sichuan Univ Coll Comp Sci Chengdu Peoples R China

ISBN: (纸本)9781728121062

Exaggerated portrait caricatures can represent peoples explicit facial characteristics. Recently, there have been increasing interests in exaggerated portrait caricatures generation from the real face images. However, existing methods randomly exaggerate facial features and the effects are not diversified. In this paper, we achieve the extraction of facial structure features and explicit geometry features using sequence-to-sequence model. The extracted facial structure features can be well applied to caricatures generation. In order to avoid destroying the recognition of the characters from exaggerating the input face, we also incorporate the matching threshold loss as a penalty term to constrain the exaggerated expression of facial features. Experiment results on the collected dataset indicate the proposed method can finally get satisfied result.

关键词： Exaggerated portrait caricatures sequence-to-sequence model structure features

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sequence-to-sequence Imputation of Missing Sensor Data 32nd

Sequence-to-Sequence Imputation of Missing Sensor Data

引用

32nd Australasian Joint Conference on Artificial Intelligence

作者： Dabrowski, Joel Janek Rahman, Ashfaqur CSIRO Data61 Brisbane Qld Australia CSIRO Data61 Hobart Tas Australia

ISBN: (纸本)9783030352882;9783030352875

Although the sequence-to-sequence (encoder-decoder) model is considered the state-of-the-art in deep learning sequence models, there is little research into using this model for recovering missing sensor data. The key challenge is that the missing sensor data problem typically comprises three sequences (a sequence of observed samples, followed by a sequence of missing samples, followed by another sequence of observed samples) whereas, the sequence-to-sequence model only considers two sequences (an input sequence and an output sequence). We address this problem by formulating a sequence-to-sequence in a novel way. A forward RNN encodes the data observed before the missing sequence and a backward RNN encodes the data observed after the missing sequence. A decoder decodes the two encoders in a novel way to predict the missing data. We demonstrate that this model produces the lowest errors in 12% more cases than the current state-of-the-art.

关键词： Imputation Interpolation LSTM Encoder-decoder model sequence-to-sequence model

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An Empathetic Conversational Agent with Attentional Mechanism

An Empathetic Conversational Agent with Attentional Mechanis...

引用

11th International Conference of Computer Communication and Informatics (ICCCI)

作者： Goel, Raman Vashisht, Sachin Dhanda, Armaan Susan, Seba Delhi Technol Univ Bawana Rd Delhi 110042 India

ISBN: (纸本)9781728158754

The number of people suffering from mental health issues like depression and anxiety have spiked enormously in recent times. Conversational agents like chatbots have emerged as an effective way for users to express their feelings and anxious thoughts and in turn obtain some empathetic reply that would relieve their anxiety. In our work, we construct two types of empathetic conversational agent models based on sequence-to-sequence modeling with and without attention mechanism. We implement the attention mechanism proposed by Bandanau et al for neural machine translation models. We train our model on the benchmark Facebook Empathetic Dialogue dataset and the BLEU scores are computed. Our empathetic conversational agent model incorporating attention mechanism generates better quality empathetic responses and is better in capturing human feelings and emotions in the conversation.

关键词： empathetic conversational agent sequence-to-sequence model attention mechanism

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L2-GEN: A Neural Phoneme Paraphrasing Approach to L2 Speech Synthesis for Mispronunciation Diagnosis 23

L2-GEN: A Neural Phoneme Paraphrasing Approach to L2 Speech ...

引用

Interspeech Conference

作者： Zhang, Daniel Yue Ganesan, Ashwinkumar Campbell, Sarah Korzekwa, Daniel Amazon Alexa AI San Francisco CA 94118 USA

In this paper, we study the problem of generating mispronounced speech mimicking non-native (L2) speakers learning English as a Second Language (ESL) for the mispronunciation detection and diagnosis (MDD) task. The paper is motivated by the widely observed yet not well addressed data sparsity issue in MDD research where both L2 speech audio and its fine-grained phonetic annotations are difficult to obtain, leading to unsatisfactory mispronunciation feedback accuracy. We propose L2-GEN, a new data augmentation framework to generate L2 phoneme sequences that capture realistic mispronunciation patterns by devising an unique machine translation-based sequence paraphrasing model. A novel diversified and preference-aware decoding algorithm is proposed to generalize L2-GEN to handle both unseen words and new learner population with very limited L2 training data. A contrastive augmentation technique is further designed to optimize MDD performance improvements with the generated synthetic L2 data. We evaluate L2-GEN on public L2-ARCTIC and SpeechOcean762 datasets. The results have shown that L2-GEN leads to up to 3.9%, and 5.0% MDD F1-score improvements in in-domain and out-ofdomain scenarios respectively.

关键词： mispronunciation diagnosis speech synthesis sequence-to-sequence model machine translation

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Robust sequence-to-sequence Acoustic modeling with Stepwise Monotonic Attention for Neural TTS 20

Robust Sequence-to-Sequence Acoustic Modeling with Stepwise ...

引用

Interspeech Conference

作者： He, Mutian Deng, Yan He, Lei Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Microsoft Beijing Peoples R China

Neural TTS has demonstrated strong capabilities to generate human-like speech with high quality and naturalness, while its generalization to out-of-domain texts is still a challenging task, with regard to the design of attention-based sequence-to-sequence acoustic modeling. Various errors occur in those inputs with unseen context, including attention collapse, skipping, repeating, etc., which limits the broader applications. In this paper, we propose a novel stepwise monotonic attention method in sequence-to-sequence acoustic modeling to improve the robustness on out-of-domain inputs. The method utilizes the strict monotonic property in TTS with constraints on monotonic hard attention that the alignments between inputs and outputs sequence must be not only monotonic but allowing no skipping on inputs. Soft attention could be used to evade mismatch between training and inference. The experimental results show that the proposed method could achieve significant improvements in robustness on out-of-domain scenarios for phoneme-based models, without any regression on the in-domain naturalness test.

关键词： sequence-to-sequence model attention speech synthesis

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Question Generation Through Transfer Learning 33rd

Question Generation Through Transfer Learning

引用

33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Liao, Yin-Hsiang Koh, Jia-Ling Natl Taiwan Normal Univ Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9783030557881;9783030557898

An automatic question generation (QG) system aims to produce questions from a text, such as a sentence or a paragraph. Traditional approaches are mainly based on heuristic, hand-crafted rules to transduce a declarative sentence to a related interrogative sentence. However, creating such a set of rules requires deep linguistic knowledge and most of these rules are language-specific. Although a data-driven approach reduces the participation of linguistic experts, to get sufficient labeled data for QG model training is still a difficult task. In this paper, we applied a neural sequence-to-sequence pointer-generator network with various transfer learning strategies to capture the underlying information of making a question, on a target domain with rare training pairs. Our experiment demonstrates the viability of domain adaptation in QG task. We also show the possibility that transfer learning is helpful in a semi-supervised approach when the amount of training pairs in the target QG dataset is not large enough.

关键词： Question generation sequence-to-sequence model Transfer learning

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EXTENDING PARROTRON: AN END-TO-END, SPEECH CONVERSION AND SPEECH RECOGNITION model FOR ATYPICAL SPEECH

EXTENDING PARROTRON: AN END-TO-END, SPEECH CONVERSION AND SP...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Doshi, Rohan Chen, Youzheng Jiang, Liyang Zhang, Xia Biadsy, Fadi Ramabhadran, Bhuvana Chu, Fang Rosenberg, Andrew Moreno, Pedro J. Google Mountain View CA 94043 USA

ISBN: (纸本)9781728176055

We present an extended Parrotron model: a single, end-to-end network that enables voice conversion and recognition simultaneously. Input spectrograms are transformed to output spectrograms in the voice of a predetermined target speaker while also generating hypotheses in a target vocabulary. We study the performance of this novel architecture, which jointly predicts speech and text, on atypical (e.g. dysarthric) speech. We show that with as little as an hour of atypical speech, speaker adaptation can yield a 77% relative reduction in Word Error Rate (WER), measured by ASR performance on the converted speech. We also show that data augmentation using a customized synthesizer built on atypical speech can provide an additional 10% relative improvement over the best speaker-adapted model. Finally, we show how these methods generalize across 8 types of atypical speech for a range of speech impairment severities.

关键词： speech normalization voice conversion speech impairments sequence-to-sequence model speech recognition

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