Adaptive Frequency Neural Networks for Dynamic Pulse and Metre Perception.

Beat induction, the means by which humans listen to music and perceive a steady pulse, is achieved via a perceptualand cognitive process. Computationally modelling this phenomenon is an open problem, especially when processing expressive shaping of the music such as tempo change.To meet this challenge we propose Adaptive Frequency Neural Networks (AFNNs), an extension of Gradient Frequency Neural Networks (GFNNs).GFNNs are based on neurodynamic models and have been applied successfully to a range of difficult music perception problems including those with syncopated and polyrhythmic stimuli. AFNNs extend GFNNs by applying a Hebbian learning rule to the oscillator frequencies. Thus the frequencies in an AFNN adapt to the stimulus through an attraction to local areas of resonance, and allow for a great dimensionality reduction in the network.Where previous work with GFNNs has focused on frequency and amplitude responses, we also consider phase information as critical for pulse perception. Evaluating the time-based output, we find significantly improved re-sponses of AFNNs compared to GFNNs to stimuli with both steady and varying pulse frequencies. This leads us to believe that AFNNs could replace the linear filtering methods commonly used in beat tracking and tempo estimationsystems, and lead to more accurate methods

Quantum Resonances of Weakly Linked, Mesoscopic, Superconducting Dots

We examine quantum properties of mesoscopic, Josephson coupled superconducting dots, in the limit that charging effects and quantization of energy levels within the dots are negligible, but quasi-particle transmission into the weak link is not. We demonstrate that quasi-particle resonances lead to current-phase relations, which deviate markedly from those of weak links connecting macroscopic superconductors. Results for the steady state dc Josephson current of two coupled dots are presented.Comment: Tex, 3 figures available on request to [email protected] (Andy Martin

Sub-gap conductance in ferromagnetic-superconducting mesoscopic structures

We study the sub-gap conductance of a ferromagnetic mesoscopic region attached to a ferromagnetic and a superconducting electrode by means of tunnel junctions. In the absence of the exchange field, the ratio $r= \gamma / \epsilon_T$ of the two tunnel junction resistances determines the behaviour of the sub-gap conductance which possesses a zero-bias peak for $r\gg 1$ and for $r\ll 1$ a peak at finite voltage. We show that the inclusion of the exchange field leads to a peak splitting for $r\ll 1$, while it shifts the zero-bias anomaly to finite voltages for $r\gg 1$.Comment: 5 pages revte

Naval History by Conspiracy Theory: The British Admiralty before the First World War and the Methodology of Revisionism

Revisionist interpretations of British naval policy in the Fisher era claim that an elaborate smoke screen was created to hide the Royal Navy’s real policies; while documents showing the true goals were systematically destroyed. By asserting this, revisionists are able to dismiss those parts of the documentary record that contradict their theories, while simultaneously excusing the lack of evidence for their theories by claiming it has been destroyed. This article shows that this methodology is misleading and untenable

Beyond the Beat: Towards Metre, Rhythm and Melody Modelling with Hybrid Oscillator Networks

In this paper we take a connectionist machine learning approach to the problem of metre perception and learning in musical signals. We present a hybrid network consisting of a nonlinear oscillator network and a recurrent neural network. The oscillator network acts as an entrained resonant filter to the musical signal. It ‘perceives’ metre by resonating to the inherent frequencies within the signal. The neural network learns the long-term temporal structures present in the signal. We show that our hybrid network outperforms previous approaches of a single layer recurrent neural network in melody prediction tasks. By perceiving metrical structure, our system is enabled to model more coherent long-term structures, and can be used in a multitude of analytic and generative scenarios, including live performance applications

Studying the Effect of Metre Perception on Rhythm and Melody Modelling with LSTMs

In this paper we take a connectionist machine learning approach to the problem of metre perception and melody learning in musical signals. We present a multi-layered network consisting of a nonlinear oscillator network and a recurrent neural network. The oscillator network acts as an entrained resonant filter to the musical signal. It `perceives' metre by resonating nonlinearly to the inherent periodicities within the signal, creating a hierarchy of strong and weak periods. The neural network learns the long-term temporal structures present in this signal. We show that this network outperforms our previous approach of a single layer recurrent neural network in a melody and rhythm prediction task. We hypothesise that our system is enabled to make use of the relatively long temporal resonance in the oscillator network output, and therefore model more coherent long-term structures. A system such as this could be used in a multitude of analytic and generative scenarios, including live performance applications

Access to resources buffers against effects of current reproduction on future ability to provide care in a burying beetle

Studies investigating the trade‐off between current and future reproduction often find that increased allocation to current reproduction is associated with a reduction in the number or quality of future offspring. In species that provide parental care, this effect on future offspring may be mediated through a reduced future ability to provide care. Here, we test this idea in the burying beetle Nicrophorus vespilloides, a species in which parents shift the cost of reproduction toward future offspring and provide elaborate parental care. We manipulated brood size to alter the costs females experienced in association with current reproduction and measured the level of parental care during a subsequent breeding attempt. Given that these beetles breed on carcasses of small vertebrates, it is important to consider confounding effects due to benefits associated with resource access during breeding. We, therefore, manipulated access to carrion and measured the level of parental care during a subsequent breeding attempt. We found that females provided the same level of care regardless of previous brood size and resource access, suggesting that neither affected future ability to provide care. This may reflect that parents feed on carrion during breeding, which may buffer against any costs of previous breeding attempts. Our results show that increased allocation to current reproduction is not necessarily associated with a reduction in future ability to provide care. Nevertheless, this may reflect unique aspects of our study system, and we encourage future work on systems where parents do not have access to a rich resource during breeding