Joint DOA and Multi-Pitch Estimation Using Block Sparsity

In this paper, we propose a novel method to estimate the fundamental frequencies and directions-of-arrival (DOA) of multi-pitch signals impinging on a sensor array. Formulating the estimation as a group sparse convex optimization problem, we use the alternating direction of multipliers method (ADMM) to estimate both temporal and spatial correlation of the array signal. By first jointly estimating both fundamental frequencies and time-of-arrivals (TOAs) for each sensor and sound source, we then form a non-linear least squares estimate to obtain the DOAs. Numerical simulations indcate the preferable performance of the proposed estimator as compared to current state-of-the-art methods

Sparse Chroma Estimation for Harmonic Audio

This work treats the estimation of the chromagram for harmonic audio signals using a block sparse reconstruction framework. Chroma has been used for decades as a key tool in audio analysis, and is typically formed using a Fourier-based framework that maps the fundamental frequency of a musical tone to its corresponding chroma. Such an approach often leads to problems with tone ambiguity, which we avoid by taking into account the harmonic structure and perceptional attributes in music. The performance of the proposed method is evaluated using real audio files, clearly showing preferable performance as compared to other commonly used methods

An Adaptive Penalty Approach to Multi-Pitch Estimation

This work treats multi-pitch estimation, and in particular the common misclassification issue wherein the pitch at half of the true fundamental frequency, here referred to as a sub-octave, is chosen instead of the true pitch. Extending on current methods which use an extension of the Group LASSO for pitch estimation, this work introduces an adaptive total variation penalty, which both enforce group- and block sparsity, and deal with errors due to sub-octaves. The method is shown to outperform current state-of-the-art sparse methods, where the model orders are unknown, while also requiring fewer tuning parameters than these. The method is also shown to outperform several conventional pitch estimation methods, even when these are virtued with oracle model orders

Sparse Multi-Pitch and Panning Estimation of Stereophonic Signals

In this paper, we propose a novel multi-pitch estimator for stereophonic mixtures, allowing for pitch estimation on multi-channel audio even if the amplitude and delay panning parameters are unknown. The presented method does not require prior knowledge of the number of sources present in the mixture, nor on the number of harmonics in each source. The estimator is formulated using a sparse signal framework, and an efficient implementation using the ADMM is introduced. Numerical simulations indicate the preferable performance of the proposed method as compared to several commonly used multi-channel single pitch estimators, and a commonly used multi-pitch estimator

Post-Earnings Announcement Drift on the Swedish Stock Market : The Effect of Corporate Governance Quality

This study examines the post-earnings announcement drift (PEAD) anomaly on the Swedish stock market. By constructing a corporate governance index based on share structure, board independence and board gender diversity, we test how the quality of firms’ corporate governance affects the drift – a link which is previously unexplored. We find no evidence of PEAD for firms with good corporate governance, while firms with bad corporate governance do experience a drift. Furthermore, a PEAD trading strategy based on bad governance firms yields significantly larger abnormal returns compared to the corresponding trading strategy for good governance firms. Our results are robust to controlling for the risk factors of the Fama-French 3-factor model. The findings support that investors tend to underreact to extreme earnings surprises reported by bad governance firms due to a higher degree of information uncertainty, while the stock price reactions are more complete for good governance firms

Hur påverkar familjeägarskap utdelningspolitiken i svenska börsbolag?

Familjeägarskap är ett utbrett fenomen på Stockholmsbörsen, där många av familjerna kan utöva kontroll över företagen de äger. Utbetalningspolitiska beslut i svenska företag kan således vara influerade av dessa ägarfamiljer. Syftet med denna studie är att undersöka familjeägarskapets påverkan på utdelningsnivån bland svenska börsnoterade företag, samt huruvida grundarfamiljer skiljer sig från andra ägarfamiljer i detta avseende. Vi diskuterar hur ägarfamiljer teoretiskt sett har goda möjligheter att expropriera minoritetsägarna, vilket kan visas genom en lägre utdelningsnivå. Samtidigt finns incitament för ägarfamiljer att hålla utdelningsnivån hög enligt rykteshypotesen och inkomsthypotesen. Urvalet består av 622 observationer från icke-finansiella företag noterade på Stockholmsbörsen mellan bokslutsåren 2014 och 2016. Studien genomförs med multipla OLS- och Tobit-regressioner. Resultaten visar att familjeägda företag ger högre utdelningar än företag som inte är familjeägda. Däremot är resultaten inte statistiskt signifikanta gällande huruvida utdelningsnivån är högre i grundarägda företag jämfört med de övriga familjeägda.

Hur påverkar familjeägarskap utdelningspolitiken i svenska börsbolag?

Familjeägarskap är ett utbrett fenomen på Stockholmsbörsen, där många av familjerna kan utöva kontroll över företagen de äger. Utbetalningspolitiska beslut i svenska företag kan således vara influerade av dessa ägarfamiljer. Syftet med denna studie är att undersöka familjeägarskapets påverkan på utdelningsnivån bland svenska börsnoterade företag, samt huruvida grundarfamiljer skiljer sig från andra ägarfamiljer i detta avseende. Vi diskuterar hur ägarfamiljer teoretiskt sett har goda möjligheter att expropriera minoritetsägarna, vilket kan visas genom en lägre utdelningsnivå. Samtidigt finns incitament för ägarfamiljer att hålla utdelningsnivån hög enligt rykteshypotesen och inkomsthypotesen. Urvalet består av 622 observationer från icke-finansiella företag noterade på Stockholmsbörsen mellan bokslutsåren 2014 och 2016. Studien genomförs med multipla OLS- och Tobit-regressioner. Resultaten visar att familjeägda företag ger högre utdelningar än företag som inte är familjeägda. Däremot är resultaten inte statistiskt signifikanta gällande huruvida utdelningsnivån är högre i grundarägda företag jämfört med de övriga familjeägda.

Hyperparameter Selection for Group-Sparse Regression: A Probabilistic Approach

This work analyzes the effects on support recovery for different choices of the hyper- or regularization parameter in LASSO-like sparse and group-sparse regression problems. The hyperparameter implicitly selects the model order of the solution, and is typically set using cross-validation (CV). This may be computationally prohibitive for large-scale problems, and also often overestimates the model order, as CV optimizes for prediction error rather than support recovery. In this work, we propose a probabilistic approach to select the hyperparameter, by quantifying the type I error (false positive rate) using extreme value analysis. From Monte Carlo simulations, one may draw inference on the upper tail of the distribution of the spurious parameter estimates, and the regularization level may be selected for a specified false positive rate. By solving the e group-LASSO problem, the choice of hyperparameter becomes independent of the noise variance. Furthermore, the effects on the false positive rate caused by collinearity in the dictionary is discussed, including ways of circumventing them. The proposed method is compared to other hyperparameter-selection methods in terms of support recovery, false positive rate, false negative rate, and computational complexity. Simulated data illustrate how the proposed method outperforms CV and comparable methods in both computational complexity and support recovery

Hyperparameter-selection for sparse regression : A probablistic approach

The choice of hyperparameter(s) notably affects the support recovery in LASSO-like sparse regression problems, acting as an implicit model order selection. Parameters are typically selected using cross-validation or various ad hoc approaches. These often overestimates the resulting model order, aiming to minimize the prediction error rather than maximizing the support recovery. In this work, we propose a probabilistic approach to selecting hyperparameters in order to maximize the support recovery, quantifying the type I error (false positive rate) using extreme value analysis, such that the regularization level is selected as an appropriate quantile. By instead solving the scaled LASSO problem, the proposed choice of hyperparameter becomes almost independent of the noise variance. Simulation examples illustrate how the proposed method outperforms both cross-validation and the Bayesian Information Criterion in terms of computational complexity and support recovery

Post-Earnings Announcement Drift on the Swedish Stock Market : The Effect of Corporate Governance Quality

This study examines the post-earnings announcement drift (PEAD) anomaly on the Swedish stock market. By constructing a corporate governance index based on share structure, board independence and board gender diversity, we test how the quality of firms’ corporate governance affects the drift – a link which is previously unexplored. We find no evidence of PEAD for firms with good corporate governance, while firms with bad corporate governance do experience a drift. Furthermore, a PEAD trading strategy based on bad governance firms yields significantly larger abnormal returns compared to the corresponding trading strategy for good governance firms. Our results are robust to controlling for the risk factors of the Fama-French 3-factor model. The findings support that investors tend to underreact to extreme earnings surprises reported by bad governance firms due to a higher degree of information uncertainty, while the stock price reactions are more complete for good governance firms