Jesper Højvang Jensen

Evaluation of Distance Measures Between Gaussian Mixture Models of MFCCs

In music similarity and in the related task of genre classification, a distance measure between Gaussian mixture models is frequently needed. We present a comparison of the Kullback-Leibler distance, the earth movers distance and the normalized L2 distance for this application. Although the normalized L2 distance was slightly inferior to the Kullback-Leibler distance with respect to classification performance, it has the advantage of obeying the triangle inequality, which allows for efficient searching.

Joint fundamental frequency and order estimation using optimal filtering

In this paper, the problem of jointly estimating the number of harmonics and the fundamental frequency of periodic signals is considered. We show how this problem can be solved using a number of methods that either are or can be interpreted as filtering methods in combination with a statistical model selection criterion. The methods in question are the classical comb filtering method, a maximum likelihood method, and some filtering methods based on optimal filtering that have recently been proposed, while the model selection criterion is derived herein from the maximum a posteriori principle. The asymptotic properties of the optimal filtering methods are analyzed and an order-recursive efficient implementation is derived. Finally, the estimators have been compared in computer simulations that show that the optimal filtering methods perform well under various conditions. It has previously been demonstrated that the optimal filtering methods perform extremely well with respect to fundamental frequency estimation under adverse conditions, and this fact, combined with the new results on model order estimation and efficient implementation, suggests that these methods form an appealing alternative to classical methods for analyzing multi-pitch signals.

A tempo-insensitive distance measure for cover song identification based on chroma features

We present a distance measure between audio files designed to identify cover songs, which are new renditions of previously recorded songs. For each song we compute the chromagram, remove phase information and apply exponentially distributed bands in order to obtain a feature matrix that compactly describes a song and is insensitive to changes in instrumentation, tempo and time shifts. As distance between two songs, we use the Frobenius norm of the difference between their feature matrices normalized to unit norm. When computing the distance, we take possible transpositions into account. In a test collection of 80 songs with two versions of each, 38% of the covers were identified. The system was also evaluated on an independent, international evaluation where it despite having much lower complexity performed on par with the winner of last year.

On Optimal Filter Designs for Fundamental Frequency Estimation

Recently, we proposed using Capons minimum variance principle to find the fundamental frequency of a periodic waveform. The resulting estimator is formed such that it maximizes the output power of a bank of filters. We present an alternative optimal single filter design and then proceed to quantify the similarities and differences between the estimators using asymptotic analysis and Monte Carlo simulations. Our analysis shows that the single filter can be expressed in terms of the optimal filterbank and that the methods are asymptotically equivalent but generally different for finite length signals.

Chapter 8 – Evaluation of the Time-Domain Speech Enhancement Filters

So far, all the chapters have been dealing with the derivation of optimal filters for speech enhancement from a subspace perspective. In this chapter, we present evaluations of the time-domain versions of these filters for both single-channel as well as multichannel scenarios. First, we evaluate the single-channel scenario, where we consider both the case where the rank of the correlation matrix of the desired signal is less than the filter length and the case where it is full rank. Then, we proceed to evaluate the time-domain multichannel filters.

Multichannel Speech Enhancement in the Frequency Domain

In this chapter, we study the multichannel speech enhancement problem in the frequency domain. By exploiting the structure of the speech subspace, we can easily estimate all convolved speech signals at the microphones with a simple complex filter. As a result, binaural noise reduction with this approach is straightforward since we can choose any two signals from the estimated convolved speech signals, which contain all the necessary spatial information for the localization of the desired source signal.

General Concept with the Diagonalization of the Speech Correlation Matrix

In this chapter, we study the general speech enhancement problem by considering the diagonalization of the speech correlation matrix in the context of the classical linear filtering technique. The entire problem is formulated as a function of the speech subspace. We define the most fundamental performance measures in this scenario. We then derive the well-known conventional filtering matrices for noise reduction and show how the nullspace of the speech correlation matrix is exploited in some of these approaches such as the MVDR and LCMV filtering.

General Concept with the Joint Diagonalization of the Speech and Noise Correlation Matrices

In the previous chapter, we showed how the eigenvalue decomposition of the speech correlation matrix can be exploited in the derivation of different types of optimal filtering matrices for the general problem of speech enhancement. This chapter attempts to show the same results but with the joint diagonalization of the speech and noise correlation matrices. We will see that there are some subtle differences between these two approaches with many more possibilities with joint diagonalization, suggesting that this tool is very natural to use in this problem.

A Bayesian Approach to the Speech Subspace Estimation

In all previous chapters, we showed the importance of the speech subspace; so it is extremely important in practice to find good ways to estimate it. One very promising possibility is the Bayesian approach based on the Stiefel manifold and the Bingham distribution. This chapter explores this avenue. We would like to point out that all the ideas presented in the following are borrowed from [1] but adapted to our specific problem of speech enhancement.