Our understanding of bird song, a model system for animal communication and the neurobiology of learning, depends critically on making reliable, validated comparisons between the complex multidimensional syllables that are used in songs. However, most assessments of song similarity are based on human inspection of spectrograms, or computational methods developed from human intuitions. Using a novel automated operant conditioning system, we collected a large corpus of zebra finches' (Taeniopygia guttata) decisions about song syllable similarity. We use this dataset to compare and externally validate similarity algorithms in widely-used publicly available software (Raven, Sound Analysis Pro, Luscinia). Although these methods all perform better than chance, they do not closely emulate the avian assessments. We then introduce a novel deep learning method that can produce perceptual similarity judgements trained on such avian decisions. We find that this new method outperforms the established methods in accuracy and more closely approaches the avian assessments. Inconsistent (hence ambiguous) decisions are a common occurrence in animal behavioural data; we show that a modification of the deep learning training that accommodates these leads to the strongest performance. We argue this approach is the best way to validate methods to compare song similarity, that our dataset can be used to validate novel methods, and that the general approach can easily be extended to other species.

Research on automatic music transcription has largely focused on multi-pitch detection; there is limited discussion on how to obtain a machine- or human-readable score transcription. In this paper, we propose a method for joint multi-pitch detection and score transcription for polyphonic piano music. The outputs of our system include both a piano-roll representation (a descriptive transcription) and a symbolic musical notation (a prescriptive transcription). Unlike traditional methods that further convert MIDI transcriptions into musical scores, we use a multitask model combined with a Convolutional Recurrent Neural Network and Sequence-to-sequence models with attention mechanisms. We propose a Reshaped score representation that outperforms a LilyPond representation in terms of both prediction accuracy and time/memory resources, and compare different input audio spectrograms. We also create a new synthesized dataset for score transcription research. Experimental results show that the joint model outperforms a single-task model in score transcription.

Sound scene geotagging is a new topic of research which has evolved from acoustic scene classification. It is motivated by the idea of audio surveillance. Not content with only describing a scene in a recording, a machine which can locate where the recording was captured would be of use to many. In this paper we explore a series of common audio data augmentation methods to evaluate which best improves the accuracy of audio geotagging classifiers. Our work improves on the state-of-the-art city geotagging method by 23% in terms of classification accuracy.

Detecting spoofing attempts of automatic speaker verification (ASV) systems is challenging, especially when using only one modeling approach. For robustness, we use both deep neural networks and traditional machine learning models and combine them as ensemble models through logistic regression. They are trained to detect logical access (LA) and physical access (PA) attacks on the dataset released as part of the ASV Spoofing and Countermeasures Challenge 2019. We propose dataset partitions that ensure different attack types are present during training and validation to improve system robustness. Our ensemble model outperforms all our single models and the baselines from the challenge for both attack types. We investigate why some models on the PA dataset strongly outperform others and find that spoofed recordings in the dataset tend to have longer silences at the end than genuine ones. By removing them, the PA task becomes much more challenging, with the tandem detection cost function (t-DCF) of our best single model rising from 0.1672 to 0.5018 and equal error rate (EER) increasing from 5.98% to 19.8% on the development set.

Many approaches have been used in bird species classification from their sound in order to provide labels for the whole of a recording. However, a more precise classification of each bird vocalization would be of great importance to the use and management of sound archives and bird monitoring. In this work, we introduce a technique that using a two step process can first automatically detect all bird vocalizations and then, with the use of `weakly' labelled recordings, classify them. Evaluations of our proposed method show that it achieves a correct classification of 75.4% when used in a synthetic dataset.

Author: Stowell, D. et al.; Genre: Conference Paper; Issued: 2016; Title: Individual identity in songbirds: Signal representations and metric learning for locating the information in complex corvid calls

Harmonic models have to be both precise and fast in order to represent the speech signal adequately and be able to process large amount of data in a reasonable amount of time.For these purposes, the full-band adaptive Harmonic Model (aHM) used by the Adaptive Iterative Refinement (AIR) algorithm has been proposed in order to accurately model the perceived characteristics of a speech signal.Even though aHM-AIR is precise, it lacks the computational efficiency that would make its use convenient for large databases.The Least Squares (LS) solution used in the original aHM-AIR accounts for most of the computational load.In a previous paper, we suggested a Peak Picking (PP) approach as a substitution to the LS solution.In order to integrate the adaptivity scheme of aHM in the PP approach, an adaptive Discrete Fourier Transform (aDFT), whose frequency basis can fully follow the variations of the f0 curve, was also proposed.In this article, we complete the previous publication by evaluating the above methods for the whole analysis process of a speech signal.Evaluations have shown an average time reduction by four times using Peak Picking and aDFT compared to the LS solution.Additionally, based on formal listening tests, when using Peak Picking and aDFT, the quality of the re-synthesis is preserved compared to the original LS-based approach.

The full-band Adaptive Harmonic Model (aHM) can be used by the Adaptive Iterative Refinement (AIR) algorithm to accurately model the perceived characteristics of a speech recording. However, the Least Squares (LS) solution used in the current aHM-AIR makes the f0refinement in AIR time consuming, limiting the use of this algorithm for large databases. In this paper, a Peak Picking (PP) approach is suggested as a substitution to the LS solution. In order to integrate the adaptivity scheme of aHM in the PP approach, an adaptive Discrete Fourier Transform (aDFT) is also suggested in this paper, whose frequency basis can fully follow the frequency variations of the f0curve. Evaluations have shown an average time reduction of 5.5 times compared to the LS solution approach, while the quality of the resynthesis is preserved compared to the original aHM-AIR.