Axel Röbel

Multiple fundamental frequency estimation of polyphonic music signals

The article is concerned with the estimation of fundamental frequencies, or F0s, in polyphonic music. We propose a new method for jointly evaluating multiple F0 hypotheses based on three physical principles, harmonicity, spectral smoothness and synchronous amplitude evolution, within a single source. Based on the generative quasiharmonic model, a set of hypothetical partial sequences is derived and an optimal assignment of the observed peaks to the hypothetical sources and noise is performed. The hypothetical partial sequences are then evaluated by a score function which formulates the guiding principles in a mathematical manner. The algorithm has been tested on a large collection of artificially mixed polyphonic samples and the results show the competitive performance of the proposed method.

On cepstral and all-pole based spectral envelope modeling with unknown model order

In this work, we investigate spectral envelope estimation for harmonic signals. We address the issue of model order selection and propose to make use of the fact that the spectral envelope is sampled by means of the harmonic structure of the signal in order to derive upper bounds for the estimator order. An experimental study is performed using synthetic test signals with various fundamental frequencies and different model structures to evaluate the performance of the envelope models. Experimental results confirm the relation between optimal model order and fundamental frequency.

Improving Lpc Spectral Envelope Extraction Of Voiced Speech By True-Envelope Estimation

n this work we address the problem of all pole spectral envelope estimation for speech signals. The currently widely used all pole spectral envelope model suffers from well-known systematic errors and more severely from model order mismatch. We will propose a procedure to first establish a band limited interpolation of the observed spectrum using a recently rediscovered true envelope estimator and then using the band limited envelope to derive an all pole envelope model named TE-LPC . The band-limited envelope that is used to derive the all pole envelope model reduces the problem of the unknown all pole model order. For the experimental investigation we propose a new perceptually motivated residual spectral peak flatness measure. The experimental results demonstrate that the proposed method significantly increases the spectral flatness for the perceptually especially important low order harmonics of voiced utterances

Dynamic Spectral Envelope Modeling for Timbre Analysis of Musical Instrument Sounds

We present a computational model of musical instrument sounds that focuses on capturing the dynamic behavior of the spectral envelope. A set of spectro-temporal envelopes belonging to different notes of each instrument are extracted by means of sinusoidal modeling and subsequent frequency interpolation, before being subjected to principal component analysis. The prototypical evolution of the envelopes in the obtained reduced-dimensional space is modeled as a nonstationary Gaussian Process. This results in a compact representation in the form of a set of prototype curves in feature space, or equivalently of prototype spectro-temporal envelopes in the time-frequency domain. Finally, the obtained models are successfully evaluated in the context of two music content analysis tasks: classification of instrument samples and detection of instruments in monaural polyphonic mixtures.

Adaptive threshold determination for spectral peak classification

A new approach to adaptive threshold selection for classification of peaks of audio spectra is presented. We here extend the previous work on classification of sinusoidal and noise peaks based on a set of spectral peak descriptors in a twofold way: on one hand we propose a compact sinusoidal model where all the modulation parameters are defined with respect to the analysis window. This fact is of great importance as we recall that the STFT spectra are closely related to the analysis window properties. On the other hand, we design a threshold selection algorithm that allows us to control the decision thresholds in an intuitive manner. The decision thresholds calculated from the relationships established between the noise power in the signal and the distributions of sinusoidal peaks assures that all peaks described as sinusoidal will be correctly classified. We also show that the threshold selection algorithm can be used for different types of analysis windows with only a slight parameter readjustment.

Adaptive additive modeling with continuous parameter trajectories

This paper investigates the estimation of time varying amplitude and phase trajectories of sinusoidal signal components. The new algorithm adaptively optimizes the parameters of a smoothly connected piecewise polynomial trajectory model. A mathematical analysis is presented that relates the user-selected meta parameters of the trajectory model (polynomial order, segment size, and smoothness at the junctions) to the analysis properties of the adaptive algorithm. It reveals new insights into the relationships between the meta parameters and the resulting time/frequency resolution of the estimate. Moreover, it is shown that for efficient optimization, the phase trajectory needs to be represented in a specific form. A new approach to address the bias/variance tradeoff of the polynomial phase trajectory model by means of regularization is presented and a complete adaptive analysis/synthesis system for sinusoidal sound components is proposed. The adaptive analysis system is investigated by means of simple tracking experiments to demonstrate the effect of the smoothness constraints and compare the results with a standard short-time Fourier transformation (STFT) base frequency estimation technique and known Cramer-Rao bounds. The potential of the adaptive strategy for the modeling of sinusoidal transients is discussed and it is shown that it achieves similar transient quality as a previously proposed method, however, with considerably lower model error. Two examples for modeling real-world signals are discussed

Analysis/synthesis comparison

We compared six sound analysis/synthesis systems used for computer music. Each system analysed the same collection of twenty-seven varied input sounds, and output the results in Sound Description Interchange Format (SDIF). We describe each system individually then compare the systems in terms of availability, the sound model(s) they use, interpolation models, noise modelling, the mutability of various sound models, the parameters that must be set to perform analysis, and characteristic artefacts. Although we have not directly compared the analysis results among the different systems, our work has made such a comparison possible.

Polyphonic musical instrument recognition based on a dynamic model of the spectral envelope

We propose a new method for detecting the musical instruments that are present in single-channel mixtures. Such a task is of interest for audio and multimedia content analysis and indexing applications. The approach is based on grouping sinusoidal trajectories according to common onsets, and comparing each groups overall amplitude evolution with a set of pre-trained probabilistic templates describing the temporal evolution of the spectral envelopes of a given set of instruments. Classification is based on either an Euclidean or a probabilistic definition of timbral similarity, both of which are compared with respect to detection accuracy.

Applying improved spectral modeling for High Quality voice conversion

In this work, accurate spectral envelope estimation is applied to Voice Conversion in order to achieve High-Quality timbre conversion. True-Envelope based estimators allow model order selection leading to an adaptation of the spectral features to the characteristics of the speaker. Optimal residual signals can also be computed following a local adaptation of the model order in terms of the F0. A new perceptual criteria is proposed to measure the impact of the spectral conversion error. The proposed envelope models show improved spectral conversion performance as well as increased converted-speech quality when compared to Linear Prediction.

Automatic Adaptation of the Time-Frequency Resolution for Sound Analysis and Re-Synthesis

We present an algorithm for sound analysis and re-synthesis with local automatic adaptation of time-frequency resolution. The reconstruction formula we propose is highly efficient, and gives a good approximation of the original signal from analyses with different time-varying resolutions within complementary frequency bands: this is a typical case where perfect reconstruction cannot in general be achieved with fast algorithms, which provides an error to be minimized. We provide a theoretical upper bound for the reconstruction error of our method, and an example of automatic adaptive analysis and re-synthesis of a music sound.