Download Automatic Music Detection in Television Productions This paper presents methods for the automatic detection of music within audio streams, in the fore- or background. The problem occurs in the context of a real-world application, namely, the analysis of TV productions w.r.t. the use of music. In contrast to plain speech/music discrimination, the problem of detecting music in TV productions is extremely difficult, since music is often used to accentuate scenes while concurrently speech and any kind of noise signals might be present. We present results of extensive experiments with a set of standard machine learning algorithms and standard features, investigate the difference between frame-level and clip-level features, and demonstrate the importance of the application of smoothing functions as a post-processing step. Finally, we propose a new feature, called Continuous Frequency Activation (CFA), especially designed for music detection, and show experimentally that this feature is more precise than the other approaches in identifying segments with music in audio streams.
Download A Cosine-Distance Based Neural Network for Music Artist Recognition Using Raw I-Vector Feature Recently, i-vector features have entered the field of Music Information Retrieval (MIR), exhibiting highly promising performance in important tasks such as music artist recognition or music similarity estimation. The i-vector modelling approach relies on a complex processing chain that limits by the use of engineered features such as MFCCs. The goal of the present paper is to make an important step towards a truly end-to-end modelling system inspired by the i-vector pipeline, to exploit the power of Deep Neural Networks1 (DNNs) to learn optimized feature spaces and transformations. Several authors have already tried to combine the power of DNNs with i-vector features, where DNNs were used for feature extraction, scoring or classification. In this paper, we try to use neural networks for the important step of i-vector post-processing and classification for the task of music artist recognition. Specifically, we propose a novel neural network for i-vector features with a cosine-distance loss function, optimized with stochastic gradient decent (SGD). We first show that current networks do not perform well with unprocessed i-vector features, and that post-processing methods such as Within-Class Covariance Normalization (WCCN) and Linear Discriminant Analysis (LDA) are crucially important to improve the i-vector representation. We further demonstrate that these linear projections (WCCN and LDA) can not be learned using general objective functions usually used in neural networks. We examine our network on a 50-class music artist recognition dataset using i-vectors extracted from frame-level timbre features. Our experiments suggest that using our network with fully unprocessed i-vectors, we can achieve the performance of the i-vector pipeline which uses i-vector post processing methods such as LDA and WCCN.
Download A Simple and Effective Spectral Feature for Speech Detection in Mixed Audio Signals We present a simple and intuitive spectral feature for detecting the presence of spoken speech in mixed (speech, music, arbitrary sounds and noises) audio signals. The feature is based on some simple observations about the appearance, in signals that contain speech, of harmonics with characteristic trajectories. Experiments with some 70 hours of radio broadcasts in five different languages demonstrate that the feature is very effective in detecting and delineating segments that contain speech, and that it also seems to be quite general and robust w.r.t. different languages.
Download Towards Multi-Instrument Drum Transcription Automatic drum transcription, a subtask of the more general automatic music transcription, deals with extracting drum instrument note onsets from an audio source. Recently, progress in transcription performance has been made using non-negative matrix factorization as well as deep learning methods. However, these works primarily focus on transcribing three drum instruments only: snare drum, bass drum, and hi-hat. Yet, for many applications, the ability to transcribe more drum instruments which make up standard drum kits used in western popular music would be desirable. In this work, convolutional and convolutional recurrent neural networks are trained to transcribe a wider range of drum instruments. First, the shortcomings of publicly available datasets in this context are discussed. To overcome these limitations, a larger synthetic dataset is introduced. Then, methods to train models using the new dataset focusing on generalization to real world data are investigated. Finally, the trained models are evaluated on publicly available datasets and results are discussed. The contributions of this work comprise: (i.) a large-scale synthetic dataset for drum transcription, (ii.) first steps towards an automatic drum transcription system that supports a larger range of instruments by evaluating and discussing training setups and the impact of datasets in this context, and (iii.) a publicly available set of trained models for drum transcription. Additional materials are available at http://ifs.tuwien.ac.at/~vogl/dafx2018.
Download Informed Selection of Frames for Music Similarity Computation In this paper we present a new method to compute frame based audio similarities, based on nearest neighbour density estimation. We do not recommend it is as a practical method for large collections because of the high runtime. Rather, we use this new method for a detailed analysis to get a deeper insight on how a bag of frames approach (BOF) determines similarities among songs, and in particular, to identify those audio frames that make two songs similar from a machine’s point of view. Our analysis reveals that audio frames of very low energy, which are of course not the most salient with respect to human perception, have a surprisingly big influence on current similarity measures. Based on this observation we propose to remove these low-energy frames before computing song models and show, via classification experiments, that the proposed frame selection strategy improves the audio similarity measure.
Download Fusing Block-level Features for Music Similarity Estimation In this paper we present a novel approach to computing music similarity based on block-level features. We first introduce three novel block-level features — the Variance Delta Spectral Pattern (VDSP), the Correlation Pattern (CP) and the Spectral Contrast Pattern (SCP). Then we describe how to combine the extracted features into a single similarity function. A comprehensive evaluation based on genre classification experiments shows that the combined block-level similarity measure (BLS) is comparable, in terms of quality, to the best current method from the literature. But BLS has the important advantage of being based on a vector space representation, which directly facilitates a number of useful operations, such as PCA analysis, k-means clustering, visualization etc. We also show that there is still potential for further improve of music similarity measures by combining BLS with another stateof-the-art algorithm; the combined algorithm then outperforms all other algorithms in our evaluation. Additionally, we discuss the problem of album and artist effects in the context of similaritybased recommendation and show that one can detect the presence of such effects in a given dataset by analyzing the nearest neighbor classification results.