Energy saving: From engineering to crop management

In greenhouse horticulture, energy costs form an increasingly larger part of the total production costs. Energy is primarily used for temperature control, reduction of air humidity, increase of light intensity and CO2 supply. Use of fossil energy can be reduced by limiting the energy demand of the system and decreasing energy losses, by intelligent climate control, by increasing the energy efficiency of the crop and by replacing fossil energy sources by sustainable ones. Energy requirement of the greenhouse can be lowered up to 20-30% by using greenhouse covers with higher insulating values and the use of energy screens. A prerequisite is that these materials should not involve considerable light loss, since this would result in a loss of production. In energy efficient greenhouse concepts, durable energy sources should be included. In (semi-)closed greenhouses, the excess of solar energy in summer is collected and stored in aquifers to be reused in winter to heat the greenhouse. Ventilation windows are closed, with specific benefits to the crop: high CO2 levels can be maintained, and temperature and humidity can be controlled to the needs of the crop. Development of new greenhouse concepts is ongoing. Current examples are greenhouse systems which convert natural energy sources such as solar energy into high-value energy such as electricity. Given a certain technical infrastructure of the greenhouse, energy consumption can be further reduced by energy efficient climate control and crop management. Essential elements are to allow fluctuating temperatures, lower crop transpiration, allow higher humidities, make efficient use of light and create fluent transitions in set points. Consequences for plant growth are related to rate of development, photosynthesis, assimilate distribution, transpiration and the occurrence of diseases or disorders. Since processes involved are complex, knowledge exchange between researchers and growers is essential to realize the goals set to reduce the energy consumption

Accelerating sparse restricted Boltzmann machine training using non-Gaussianity measures

In recent years, sparse restricted Boltzmann machines have gained popularity as unsupervised feature extractors. Starting from the observation that their training process is biphasic, we investigate how it can be accelerated: by determining when it can be stopped based on the non-Gaussianity of the distribution of the model parameters, and by increasing the learning rate when the learnt filters have locked on to their preferred configurations. We evaluated our approach on the CIFAR-10, NORB and GTZAN datasets

Learning content-based metrics for music similarity

In this abstract, we propose a method to learn application-specific content-based metrics for music similarity using unsupervised feature learning and neighborhood components analysis. Multiple-timescale features extracted from music audio are embedded into a Euclidean metric space, so that the distance between songs reflects their similarity. We evaluated the method on the GTZAN and Magnatagatune datasets

Multiscale approaches to music audio feature learning

Content-based music information retrieval tasks are typically solved with a two-stage approach: features are extracted from music audio signals, and are then used as input to a regressor or classifier. These features can be engineered or learned from data. Although the former approach was dominant in the past, feature learning has started to receive more attention from the MIR community in recent years. Recent results in feature learning indicate that simple algorithms such as K-means can be very effective, sometimes surpassing more complicated approaches based on restricted Boltzmann machines, autoencoders or sparse coding. Furthermore, there has been increased interest in multiscale representations of music audio recently. Such representations are more versatile because music audio exhibits structure on multiple timescales, which are relevant for different MIR tasks to varying degrees. We develop and compare three approaches to multiscale audio feature learning using the spherical K-means algorithm. We evaluate them in an automatic tagging task and a similarity metric learning task on the Magnatagatune dataset

Piano Genie

We present Piano Genie, an intelligent controller which allows non-musicians to improvise on the piano. With Piano Genie, a user performs on a simple interface with eight buttons, and their performance is decoded into the space of plausible piano music in real time. To learn a suitable mapping procedure for this problem, we train recurrent neural network autoencoders with discrete bottlenecks: an encoder learns an appropriate sequence of buttons corresponding to a piano piece, and a decoder learns to map this sequence back to the original piece. During performance, we substitute a user's input for the encoder output, and play the decoder's prediction each time the user presses a button. To improve the intuitiveness of Piano Genie's performance behavior, we impose musically meaningful constraints over the encoder's outputs.Comment: Published as a conference paper at ACM IUI 201

Audio-based music classification with a pretrained convolutional network

Recently the ‘Million Song Dataset’, containing audio features and metadata for one million songs, was made available. In this paper, we build a convolutional network that is then trained to perform artist recognition, genre recognition and key detection. The network is tailored to summarize the audio features over musically significant timescales. It is infeasible to train the network on all available data in a supervised fashion, so we use unsupervised pretraining to be able to harness the entire dataset: we train a convolutional deep belief network on all data, and then use the learnt parameters to initialize a convolutional multilayer perceptron with the same architecture. The MLP is then trained on a labeled subset of the data for each task. We also train the same MLP with randomly initialized weights. We find that our convolutional approach improves accuracy for the genre recognition and artist recognition tasks. Unsupervised pretraining improves convergence speed in all cases. For artist recognition it improves accuracy as well

The spectral radius remains a valid indicator of the echo state property for large reservoirs

In the field of Reservoir Computing, scaling the spectral radius of the weight matrix of a random recurrent neural network to below unity is a commonly used method to ensure the Echo State Property. Recently it has been shown that this condition is too weak. To overcome this problem, other more involved - sufficient conditions for the Echo State Property have been proposed. In this paper we provide a large-scale experimental verification of the Echo State Property for large recurrent neural networks with zero input and zero bias. Our main conclusion is that the spectral radius method remains a valid indicator of the Echo State Property; the probability that the Echo State Property does not hold, drops for larger networks with spectral radius below unity, which are the ones of practical interest