In recent years, the task of Automatic Music Transcription (AMT), whereby various attributes of music notes are estimated from audio, has received increasing attention. At the same time, the related task of Multi-Pitch Estimation (MPE) remains a challenging but necessary component of almost all AMT approaches, even if only implicitly. In the context of AMT, pitch information is typically quantized to the nominal pitches of the Western music scale. Even in more general contexts, MPE systems typically produce pitch predictions with some degree of quantization. In certain applications of AMT, such as Guitar Tablature Transcription (GTT), it is more meaningful to estimate continuous-valued pitch contours. Guitar tablature has the capacity to represent various playing techniques, some of which involve pitch modulation. Contemporary approaches to AMT do not adequately address pitch modulation, and offer only less quantization at the expense of more model complexity. In this paper, we present a GTT formulation that estimates continuous-valued pitch contours, grouping them according to their string and fret of origin. We demonstrate that for this task, the proposed method significantly improves the resolution of MPE and simultaneously yields tablature estimation results competitive with baseline models.

Object-based representations of audio content are increasingly used in entertainment systems to deliver immersive and personalized experiences. Efficient storage and transmission of such content can be achieved by joint object coding algorithms that convey a reduced number of downmix signals together with parametric side information that enables object reconstruction in the decoder. This paper presents an approach to improve the performance of joint object coding by adding one or more decorrelators to the decoding process. Listening test results illustrate the performance as a function of the number of decorrelators. The method is adopted as part of the Dolby AC-4 system standardized by ETSI.

Research on speech/music classification of digital audio has been both popular in academia, and increasingly utilized in industry. Most of the usual methods use carefully hand-crafted features with Gaussian Mixture Models. To get best performance, some of the features necessitate a long latency due to look ahead, or/and a long onset error. This paper aims to have a different approach to the problem by exploring some of the latest trends in machine learning that have resulted in improvements in other fields. Specifically, it is shown that we can achieve comparable performance by only analyzing segments in the order of tens of milliseconds without the use of following or previous audio. This is done by using a method that allows automatic generation of arbitrarily many features from preprocessed spectrograms.

Compressed sensing (CS) samples signals at a much lower rate than the Nyquist rate if they are sparse in some basis. In this paper, the CS methodology is applied to sinusoidally modeled audio signals. As this model is sparse by definition in the frequency domain (being equal to the sum of a small number of sinusoids), we investigate whether CS can be used to encode audio signals at low bitrates. In contrast to encoding the sinusoidal parameters (amplitude, frequency, phase) as current state-of-the-art methods do, we propose encoding few randomly selected samples of the time-domain description of the sinusoidal component (per signal segment). The potential of applying compressed sensing both to single-channel and multi-channel audio coding is examined. The listening test results are encouraging, indicating that the proposed approach can achieve comparable performance to that of state-of-the-art methods. Given that CS can lead to novel coding systems where the sampling and compression operations are combined into one low-complexity step, the proposed methodology can be considered as an important step towards applying the CS framework to audio coding applications.

Sinusoidal modeling of audio requires the model parameters to be selected by analyzing the original signal spectrum. This paper proposes two improvements in sinusoidal selection by considering how psychoacoustic masking curves can be calculated using a top-down strategy in certain situations. First, a non-iterative component selection method to be used in combination with an added residual signal is presented. Tests indicate computational gain and quality increase when the method is used with a noise-synthesized residual. Secondly, the estimation of the masking curve in binaural listening when signals are panned is considered. Tests show that knowledge of the degree of panning is beneficial when heavy panning is applied to simultaneously rendered audio object signals.

A binaural audio synthesis system based on sinusoidal modeling is proposed for spatial, low-bitrate audio coding utilized for example in teleconference applications. The system transmits monaural sinusoidal parameters of a downmix signal, from which the left and right binaural signals are synthesized according to the directional metadata at the receiver. Typical sinusoidal synthesis methods, as well as the effectiveness of a monaural frequency masking model, are evaluated in binaural context. Furthermore, a method for binaural noise residual synthesis and efficiency improvements for HRTF parameter acquisition are suggested. Tests utilizing speech signals indicate that sinusoidal modeling is an attractive technique for applications such as the proposed one.

This paper focuses on a comparative statistical analysis of the performance of VoIP calls under various situations, namely, during a handover and under different background traffic conditions at a wireless access point (AP). Using empirical-based measurements, it demonstrates that these network conditions exhibit distinct statistical behaviour, in terms of SNR, packet losses and end-to-end delays, and thus, impact the VoIP user quality in a different manner. The analysis shows that both network conditions and codec type, as well as their interaction, have a significant effect on the PESQ MOS values. Moreover, it indicates statistically highly significant differences between the estimations of the PESQ and E-model. Finally, it highlights the benefits of the packet loss concealment of the AMR 12.2kb/s under these network conditions.

In this paper, the compressed sensing (CS) methodology is applied to the harmonic part of sinusoidally-modeled audio signals. As this part of the model is sparse by definition in the frequency domain, we investigate how CS can be used to encode this signal at low bitrates, instead of encoding the sinusoidal parameters (amplitude, frequency, phase) as current state-of-the-art methods do. We extend our previous work by considering an improved system model, by comparing our model to other schemes, and exploring the effect of incorrectly reconstructed frames. We show that encouraging results can be obtained by our approach, although inferior at this point compared to state-of-the-art. Good performance is obtained using 24 bits per sinusoid as indicated by our listening tests.

The localization of virtual sources generated with different two-dimensional (2-D) multichannel reproduction systems has been studied by means of auditory model simulations and listening tests. The reproduction was implemented with typical five- and eight-channel loudspeaker setups. The microphone systems used were first- and second-order Ambisonics as well as a spaced microphone technique. Pair-wise panning was also studied. The results show that the auditory model can be used in the prediction of perceived direction in multichannel sound reproduction near the median plane. Some systematic deviations between the model predictions and the listening test results were found farther from the median plane. The frequency-dependent capability to produce narrow-band virtual sources to targeted directions is reported for the studied systems.