Maximum-Length Sequences

Maximum-Length Sequences (MLS) are pseudorandom signals that can be used to excite a linear system, the output of which can be deconvolved to determine the system’s impulse response. They are spectrally white and have a unit-impulse autocorrelation function. This method of testing is preferable to impulse testing as signal energy is spread out over time, providing greater SNR and reducing nonlinear effects. This package includes a short tutorial, some MATLAB examples and the source code.

runme.m – In-context usage, demonstrating an MLS sequence convolved with a room impulse response, then deconvolved to recover the impulse response.

GenerateMLSSequence.m  – Generates an MLS sequence of predetermined order and repetition.

AnalyseMLSSequence.m – Analyses an MLS sequence that has been convolved with the system under test.



From Accurate estimation of glottal closing instants (GCIs) and opening instants (GOIs) is important for speech processing applications that benefit from glottal-synchronous processing including pitch tracking, prosodic speech modification, speech dereverberation, synthesis and study of pathological voice. We propose the Yet Another GCI/GOI Algorithm (YAGA) to detect GCIs from speech signals by employing multiscale analysis, the group delay function, and N-best dynamic programming. A novel GOI detector based upon the consistency of the candidates’ closed quotients relative to the estimated GCIs is also presented. Particular attention is paid to the precise definition of the glottal closed phase, which we define as the analysis interval that produces minimum deviation from an all-pole model of the speech signal with closed-phase linear prediction (LP). A reference algorithm analyzing both electroglottograph (EGG) and speech signals is described for evaluation of the proposed speech-based algorithm. In addition to the development of a GCI/GOI detector, an important outcome of this work is in demonstrating that GOIs derived from the EGG signal are not necessarily well-suited to closed-phase LP analysis. Evaluation of YAGA against the APLAWD and SAM databases show that GCI identification rates of up to 99.3% can be achieved with an accuracy of 0.3 ms and GOI detection can be achieved equally reliably with an accuracy of 0.5 ms. – requires Mike Brookes’s Voicebox library.