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Toward Interpretable Music Tagging with Self-AttentionJun 12 2019Self-attention is an attention mechanism that learns a representation by relating different positions in the sequence. The transformer, which is a sequence model solely based on self-attention, and its variants achieved state-of-the-art results in many ... More Word-level Speech Recognition with a Dynamic LexiconJun 10 2019We propose a direct-to-word sequence model with a dynamic lexicon. Our word network constructs word embeddings dynamically from the character level tokens. The word network can be integrated seamlessly with arbitrary sequence models including Connectionist ... More "Did You Hear That?" Learning to Play Video Games from Audio CuesJun 10 2019Jun 11 2019Game-playing AI research has focused for a long time on learning to play video games from visual input or symbolic information. However, humans benefit from a wider array of sensors which we utilise in order to navigate the world around us. In particular, ... More Deep Unsupervised Drum TranscriptionJun 09 2019We introduce DrummerNet, a drum transcription system that is trained in an unsupervised manner. DrummerNet does not require any ground-truth transcription, and with the data-scalability of deep neural networks, it learns from a large unlabelled dataset. ... More Deep Music Analogy Via Latent Representation DisentanglementJun 09 2019Analogy is a key solution to automated music generation, featured by its ability to generate both natural and creative pieces based on only a few examples. In general, an analogy is made by partially transferring the music abstractions, i.e., high-level ... More Audio tagging with noisy labels and minimal supervisionJun 07 2019This paper introduces Task 2 of the DCASE2019 Challenge, titled "Audio tagging with noisy labels and minimal supervision". This task was hosted on the Kaggle platform as "Freesound Audio Tagging 2019". The task evaluates systems for multi-label audio ... More Singing voice separation: a study on training dataJun 06 2019In the recent years, singing voice separation systems showed increased performance due to the use of supervised training. The design of training datasets is known as a crucial factor in the performance of such systems. We investigate on how the characteristics ... More Complex Evolution Recurrent Neural Networks (ceRNNs)Jun 05 2019Unitary Evolution Recurrent Neural Networks (uRNNs) have three attractive properties: (a) the unitary property, (b) the complex-valued nature, and (c) their efficient linear operators. The literature so far does not address -- how critical is the unitary ... More MelNet: A Generative Model for Audio in the Frequency DomainJun 04 2019Capturing high-level structure in audio waveforms is challenging because a single second of audio spans tens of thousands of timesteps. While long-range dependencies are difficult to model directly in the time domain, we show that they can be more tractably ... More A Surprising Density of Illusionable Natural SpeechJun 03 2019Recent work on adversarial examples has demonstrated that most natural inputs can be perturbed to fool even state-of-the-art machine learning systems. But does this happen for humans as well? In this work, we investigate: what fraction of natural instances ... More Evaluating Non-aligned Musical Score Transcriptions with MV2HJun 03 2019The original MV2H metric was designed to evaluate systems which transcribe from an input audio (or MIDI) piece to a complete musical score. However, it requires both the transcribed score and the ground truth score to be time-aligned with the input. Some ... More What does a Car-ssette tape tell?May 31 2019Captioning has attracted much attention in image and video understanding while little work examines audio captioning. This paper contributes a manually-annotated dataset on car scene, in extension to a previously published hospital audio captioning dataset. ... More Real-Time Adversarial AttacksMay 31 2019In recent years, many efforts have demonstrated that modern machine learning algorithms are vulnerable to adversarial attacks, where small, but carefully crafted, perturbations on the input can make them fail. While these attack methods are very effective, ... More Lattice-based lightly-supervised acoustic model trainingMay 30 2019In the broadcast domain there is an abundance of related text data and partial transcriptions, such as closed captions and subtitles. This text data can be used for lightly supervised training, in which text matching the audio is selected using an existing ... More Speaker Anonymization Using X-vector and Neural Waveform ModelsMay 30 2019The social media revolution has produced a plethora of web services to which users can easily upload and share multimedia documents. Despite the popularity and convenience of such services, the sharing of such inherently personal data, including speech ... More Texture Selection for Automatic Music Genre ClassificationMay 28 2019Music Genre Classification is the problem of associating genre-related labels to digitized music tracks. It has applications in the organization of commercial and personal music collections. Often, music tracks are described as a set of timbre-inspired ... More Two-level Explanations in Music Emotion RecognitionMay 28 2019Current ML models for music emotion recognition, while generally working quite well, do not give meaningful or intuitive explanations for their predictions. In this work, we propose a 2-step procedure to arrive at spectrogram-level explanations that connect ... More Ensemble-based cover song detectionMay 28 2019Audio-based cover song detection has received much attention in the MIR community in the recent years. To date, the most popular formulation of the problem has been to compare the audio signals of two tracks and to make a binary decision based on this ... More Reconstructing faces from voicesMay 25 2019Voice profiling aims at inferring various human parameters from their speech, e.g. gender, age, etc. In this paper, we address the challenge posed by a subtask of voice profiling - reconstructing someone's face from their voice. The task is designed to ... More Fast computation of loudness using a deep neural networkMay 24 2019The present paper introduces a deep neural network (DNN) for predicting the instantaneous loudness of a sound from its time waveform. The DNN was trained using the output of a more complex model, called the Cambridge loudness model. While a modern PC ... More FastSpeech: Fast, Robust and Controllable Text to SpeechMay 22 2019Neural network based end-to-end text to speech (TTS) has significantly improved the quality of synthesized speech. Prominent methods (e.g., Tacotron 2) usually first generate mel-spectrogram from text, and then synthesize speech from mel-spectrogram using ... More FastSpeech: Fast, Robust and Controllable Text to SpeechMay 22 2019May 29 2019Neural network based end-to-end text to speech (TTS) has significantly improved the quality of synthesized speech. Prominent methods (e.g., Tacotron 2) usually first generate mel-spectrogram from text, and then synthesize speech from mel-spectrogram using ... More FastSpeech: Fast, Robust and Controllable Text to SpeechMay 22 2019May 23 2019Neural network based end-to-end text to speech (TTS) has significantly improved the quality of synthesized speech. Prominent methods (e.g., Tacotron 2) usually first generate mel-spectrogram from text, and then synthesize speech from mel-spectrogram using ... More Bayesian Pitch Tracking Based on the Harmonic ModelMay 21 2019Fundamental frequency is one of the most important characteristics of speech and audio signals. Harmonic model-based fundamental frequency estimators offer a higher estimation accuracy and robustness against noise than the widely used autocorrelation-based ... More Parallel Neural Text-to-SpeechMay 21 2019Jun 05 2019In this work, we propose a non-autoregressive seq2seq model that converts text to spectrogram. It is fully convolutional and obtains about 46.7 times speed-up over Deep Voice 3 at synthesis while maintaining comparable speech quality using a WaveNet vocoder. ... More Parallel Neural Text-to-SpeechMay 21 2019In this work, we propose a non-autoregressive seq2seq model that converts text to spectrogram. It is fully convolutional and obtains about 17.5 times speed-up over Deep Voice 3 at synthesis while maintaining comparable speech quality using a WaveNet vocoder. ... More Robust sound event detection in bioacoustic sensor networksMay 20 2019Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, ... More Independent Vector Analysis with more Microphones than SourcesMay 20 2019May 22 2019We extend frequency-domain blind source separation based on independent vector analysis to the case where there are more microphones than sources. The signal is modelled as non-Gaussian sources in a Gaussian background. The proposed algorithm is based ... More Human Vocal Sentiment AnalysisMay 19 2019In this paper, we use several techniques with conventional vocal feature extraction (MFCC, STFT), along with deep-learning approaches such as CNN, and also context-level analysis, by providing the textual data, and combining different approaches for improved ... More Dance Hit Song PredictionMay 17 2019Record companies invest billions of dollars in new talent around the globe each year. Gaining insight into what actually makes a hit song would provide tremendous benefits for the music industry. In this research we tackle this question by focussing on ... More Multi Web Audio Sequencer: Collaborative Music MakingMay 16 2019Recent advancements in web-based audio systems have enabled sufficiently accurate timing control and real-time sound processing capabilities. Numerous specialized music tools, as well as digital audio workstations, are now accessible from browsers. Features ... More End-to-End Multi-Channel Speech SeparationMay 15 2019The end-to-end approach for single-channel speech separation has been studied recently and shown promising results. This paper extended the previous approach and proposed a new end-to-end model for multi-channel speech separation. The primary contributions ... More End-to-End Multi-Channel Speech SeparationMay 15 2019May 28 2019The end-to-end approach for single-channel speech separation has been studied recently and shown promising results. This paper extended the previous approach and proposed a new end-to-end model for multi-channel speech separation. The primary contributions ... More