29,538 research outputs found
A synthesis-based method for pitch extraction
A synthesis-based method for pitch extraction of the speech signal is proposed. The method synthesizes a number of log power spectra for different values of fundamental frequency and compares them with the log power spectrum of the input speech segment. The average magnitude (AM) difference between the two spectra is used for comparison. The value of fundamental frequency that gives the minimum AM difference between the synthesized spectrum and the input spectrum is chosen as the estimated value of fundamental frequency. The voiced/unvoiced decision is made on the basis of the value of the AM difference at the minimum. For synthesizing the log power spectrum, the speech signal is assumed to be the output of an all-pole filter. The transfer function of the all-pole filter is estimated from the input speech segment by using the autocorrelation method of linear prediction. The synthesis-based method is tried out on real speech data and the results are discussed
Extraction of vocal-tract system characteristics from speechsignals
We propose methods to track natural variations in the characteristics of the vocal-tract system from speech signals. We are especially interested in the cases where these characteristics vary over time, as happens in dynamic sounds such as consonant-vowel transitions. We show that the selection of appropriate analysis segments is crucial in these methods, and we propose a selection based on estimated instants of significant excitation. These instants are obtained by a method based on the average group-delay property of minimum-phase signals. In voiced speech, they correspond to the instants of glottal closure. The vocal-tract system is characterized by its formant parameters, which are extracted from the analysis segments. Because the segments are always at the same relative position in each pitch period, in voiced speech the extracted formants are consistent across successive pitch periods. We demonstrate the results of the analysis for several difficult cases of speech signals
RawNet: Fast End-to-End Neural Vocoder
Neural networks based vocoders have recently demonstrated the powerful
ability to synthesize high quality speech. These models usually generate
samples by conditioning on some spectrum features, such as Mel-spectrum.
However, these features are extracted by using speech analysis module including
some processing based on the human knowledge. In this work, we proposed RawNet,
a truly end-to-end neural vocoder, which use a coder network to learn the
higher representation of signal, and an autoregressive voder network to
generate speech sample by sample. The coder and voder together act like an
auto-encoder network, and could be jointly trained directly on raw waveform
without any human-designed features. The experiments on the Copy-Synthesis
tasks show that RawNet can achieve the comparative synthesized speech quality
with LPCNet, with a smaller model architecture and faster speech generation at
the inference step.Comment: Submitted to Interspeech 2019, Graz, Austri
Singing voice correction using canonical time warping
Expressive singing voice correction is an appealing but challenging problem.
A robust time-warping algorithm which synchronizes two singing recordings can
provide a promising solution. We thereby propose to address the problem by
canonical time warping (CTW) which aligns amateur singing recordings to
professional ones. A new pitch contour is generated given the alignment
information, and a pitch-corrected singing is synthesized back through the
vocoder. The objective evaluation shows that CTW is robust against
pitch-shifting and time-stretching effects, and the subjective test
demonstrates that CTW prevails the other methods including DTW and the
commercial auto-tuning software. Finally, we demonstrate the applicability of
the proposed method in a practical, real-world scenario
Data-driven Extraction of Intonation Contour Classes
In this paper we introduce the first steps towards a new datadriven method for extraction of intonation events that does not require any prerequisite prosodic labelling. Provided with data segmented on the syllable constituent level it derives local and global contour classes by stylisation and subsequent clustering of the stylisation parameter vectors. Local contour classes correspond to pitch movements connected to one or several syllables and determine the local f0 shape. Global classes are connected to intonation phrases and determine the f0 register. Local classes initially are derived for syllabic segments, which are then concatenated incrementally by means of statistical language modelling of co-occurrence patterns. Due to its generality the method is in principal language independent and potentially capable to deal also with other aspects of prosody than intonation. 1
Kalman tracking of linear predictor and harmonic noise models for noisy speech enhancement
This paper presents a speech enhancement method based on the tracking and denoising of the formants of a linear prediction (LP) model of the spectral envelope of speech and the parameters of a harmonic noise model (HNM) of its excitation. The main advantages of tracking and denoising the prominent energy contours of speech are the efficient use of the spectral and temporal structures of successive speech frames and a mitigation of processing artefact known as the âmusical noiseâ or âmusical tonesâ.The formant-tracking linear prediction (FTLP) model estimation consists of three stages: (a) speech pre-cleaning based on a spectral amplitude estimation, (b) formant-tracking across successive speech frames using the Viterbi method, and (c) Kalman filtering of the formant trajectories across successive speech frames.The HNM parameters for the excitation signal comprise; voiced/unvoiced decision, the fundamental frequency, the harmonicsâ amplitudes and the variance of the noise component of excitation. A frequency-domain pitch extraction method is proposed that searches for the peak signal to noise ratios (SNRs) at the harmonics. For each speech frame several pitch candidates are calculated. An estimate of the pitch trajectory across successive frames is obtained using a Viterbi decoder. The trajectories of the noisy excitation harmonics across successive speech frames are modeled and denoised using Kalman filters.The proposed method is used to deconstruct noisy speech, de-noise its model parameters and then reconstitute speech from its cleaned parts. Experimental evaluations show the performance gains of the formant tracking, pitch extraction and noise reduction stages
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