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| 1.
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論文
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Horita, Akihide ; Nakayama, Kenji ; Hirano, Akihiro
概要:
金沢大学大学院自然科学研究科情報システム<br />Feed-Forward (FF-) and Feed-Back (FB-) structures have been proposed for Blind Source Separati
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on (BSS). The FF-BSS systems have some degrees of freedom in the solution space, and signal distortion is likely to occur in convolutive mixtures. On the other hand, the FB-BSS structure does not cause signal distortion. However, it requires a condition on the propagation delays in the mixing process. In this paper, source separation performance in the FB-BSS is theoretically analyzed taking the propagation delays into account. Simulation is carried out by using white signals and speech signals as the signal sources. The FF-BSS system and the FB-BSS system are compared. Even though the FB-BSS can provide good separation performance, there exits some limitation on location of the signal sources and the sensors. © Springer-Verlag Berlin Heidelberg 2007.
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| 2.
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Horita, Akihide ; Nakayama, Kenji ; Hirano, Akihiro ; Dejima, Yasuhiro
概要:
Source separation and signal distortion in three kinds of BSSs with convolutive mixture are analyzed. They include a fee
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dforward BSS, trained in the time domain and in the frequency domain, and a feedback BSS, trained in the time domain. First, an evaluation measure of signal distortion is discussed. Second, conditions for source separation and distortion free are derived. Based on these conditions, source separation and signal distortion are analyzed. The feedforward BSS has some degree of freedom, and the output spectrum can be changed. The feedforward BSS, trained in the frequency domain, has weighting effect, which can suppress signal distortion. This weighting is, however, effective only when the source spectra are similar to each other. Since, the feedforward BSS, trained in the time domain, does not have any constraints on signal distortion free, its output signals can be easily distorted. A new learning algorithm with a distortion free constraint is proposed. On the other hand, the feedback BSS can satisfy both source separation and distortion free conditions simultaneously. Simulation results support the theoretical analysis. © 2005 IEEE.
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| 3.
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Horita, Akihide ; Nakayama, Kenji ; Hirano, Akihiro ; Dejima, Yasuhiro
概要:
金沢大学大学院自然科学研究科<br />Source separation and signal distortion are theoretically analyzed for the FF-BSS systems implemente
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d in both the time and frequency domains and the FB-BSS system. The FF-BSS systems have some degree of freedom, and cause some signal distortion. The FB-BSS has a unique solution for complete separation and distortion free. Next, the condition for complete separation and distortion free is derived for the FF-BSS systems. This condition is applied to the learning algorithms. Computer simulations by using speech signals and stationary colored signals are carried out for the conventional methods and the new learning algorithms employing the proposed distortion free constraint. The proposed method can drastically suppress signal distortion, while maintaining high separation performance. The FB-BSS system also demonstrates good performances. The FF-BSS systems and the FB-BSS system are compared based on the transmission time difference in the mixing process. Location of the signal sources and the sensors are rather limited in the FB-BSS system. © 2006 IEEE.
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| 4.
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Nakayama, Kenji ; Hirano, Akihiro ; Horita, Akihide
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| 5.
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Nakayama, Kenji ; Hirano, Akihiro ; Horita, Akihide
概要:
First, convergence properties in blind source separation (BSS) of convolutive mixtures are analyzed. A fully recurrent network is taken into account. Convergence is highly dependent on relation among signal source power,
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transmission gain and delay in a mixing process. Especially, reverberations degrade separation performance. Second, a learning algorithm is proposed for this situation. In an unmixing block, feedback paths have an FIR filter. The filter coefficients are updated through the gradient algorithm starting from zero initial guess. The correction is exponentially scaled along the tap number. In other words, stepsize is exponentially weighted. Since the filter coefficients with a long delay are easily affected by the reverberations, their correction are suppressed. Exponential weighting is automatically adjusted by approximating an envelop of the filter coefficients in a learning process. Through simulation, good separation performance, which is the same as in no reverberations condition, can be achieved by the proposed method.
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| 6.
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論文
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Horita, Akihide ; Nakayama, Kenji ; Hirano, Akihiro
概要:
金沢大学理工研究域 電子情報学系<br />FeedForward (FF-) Blind Source Separation (BSS) systems have some degree of freedom in the solutio
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n space. Therefore, signal distortion is likely to occur. First, a criterion for the signal distortion is discussed. Properties of conventional methods proposed to suppress the signal distortion are analyzed. Next, a general condition for complete separation and distortion-free is derived for multi-channel FF-BSS systems. This condition is incorporated in learning algorithms as a distortion-free constraint. Computer simulations using speech signals and stationary colored signals are performed for the conventional methods and for the new learning algorithms employing the proposed distortion-free constraint. The proposed method can well suppress signal distortion, while maintaining a high source separation performance.
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| 7.
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論文
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Horita, Akihide ; Nakayama, Kenji ; Hirano, Akihiro
概要:
金沢大学理工研究域 電子情報学系<br />出版者許諾要件を調査中.
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| 8.
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Horita, Akihide ; Nakayama, Kenji ; Hirano, Akihiro ; Dejima, Yasuhiro
概要:
金沢大学理工研究域 電子情報学系<br />In Blind Source Separation (BSS), a eparation block is trained so as to make the output signals to
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be statistically independent. In this case, the independency is able to be increased by changing frequency response of the output signals, resulting in signal distortion. Especially, a feed-forward BSS (FF-BSS) has some degree of freedom in the separation block, and the signal distortion will be caused. The signal distortion is evaluated as difference between the output signal and the signal source in the measured signal. Some equations are derived from the conditions of complete separation and signal distortion free. They are used as the distortion free constraint in the conventional learning process [11]. On the other hand, a feedback BSS (FB-BSS) has a solution, which can satisfy both complete separation and distortion free. In this paper, the learning algorithm with the distortion free constraint is applied to the FF-BSS in time domain. Many kinds of signal sources are used in simulation in order to compare the proposed method and the conventional, in which difference between the output signals and the measured signals is included in the cost function [4]. Furthermore, the FB-BSS is also evaluated.ブラインド信号源分離では(BSS) は分離回路がその出力信号が統計的に独立になるように学習される．この場合，出力信号の周波数特性が変化することにより，独立性が高まることもあるので，信号歪みが生じる可能性がある．特に，フィードフォワード形BSS(FF-BSS)は分離回路における自由度が高く，信号歪みを生じる可能性がある．信号歪みの基準を観測信号に含まれる信号源と考え，完全分離の条件と信号無歪の条件から導かれた制約条件を学習に加味する信号歪み抑制学習法を提案した[11]．信号源をsi，観測信号をxi，出力信号をyi とするとき，信号を分離するとともにyi をxi におけるsi 成分に近づけることができる．これに対し，観測信号と出力信号の差を評価関数に追加する従来法では，観測信号に含まれる複数の信号源の影響で信号源分離が充分ではない．一方，フィードバック形BSS(FBBSS)では，信号源分離と信号歪み抑制の条件を同時に満たす回が存在する．本稿では信号歪み抑制学習法を時間領域で学習するFF-BSS に適用し，種々の信号源を 使って従来方式[4] と比較することによりその特性を解 析する．同時に，FB-BSS の有効性も検証する．
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| 9.
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論文
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Horita, Akihide ; Nakayama, Kenji ; Hirano, Akihiro
概要:
金沢大学理工研究域 電子情報学系
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| 10.
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論文
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Horita, Akihide ; Nakayama, Kenji ; Hirano, Akihiro ; Dejima, Yasuhiro
概要:
金沢大学理工研究域 電子情報学系<br />Source separation and signal distortion in three kinds of BSSs with convolutive mixture are analyz
…
ed. They include a feedforward BSS, trained in the time domain and in the frequency domain, and a feedback BSS, trained in the time domain. First, an evaluation measure of signal distortion is discussed. Second, conditions for source separation and distortion free are derived. Based on these conditions, source separation and signal distortion are analyzed. The feedforward BSS has some degree of freedom, and the output spectrum can be changed. The feedforward BSS, trained in the frequency domain, has weighting effect, which can suppress signal distortion. This weighting is, however, effective only when the source spectra are similar to each other. Since, the feedforward BSS, trained in the time domain, does not have any constraints on signal distortion free, its output signals can be easily distorted. A new learning algorithm with a distortion free constraint is proposed. On the other hand, the feedback BSS can satisfy both source separation and distortion free conditions simultaneously. Simulation results support the theoretical analysis. © 2005 IEEE.
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| 11.
論文 |
論文
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Horita, Akihide ; Nakayama, Kenji ; Hirano, Akihiro ; Dejima, Yasuhiro
概要:
金沢大学理工研究域 電子情報学系<br />Source separation and signal distortion are theoretically analyzed for the FF-BSS systems implemen
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ted in both the time and frequency domains and the FB-BSS system. The FF-BSS systems have some degree of freedom, and cause some signal distortion. The FB-BSS has a unique solution for complete separation and distortion free. Next, the condition for complete separation and distortion free is derived for the FF-BSS systems. This condition is applied to the learning algorithms. Computer simulations by using speech signals and stationary colored signals are carried out for the conventional methods and the new learning algorithms employing the proposed distortion free constraint. The proposed method can drastically suppress signal distortion, while maintaining high separation performance. The FB-BSS system also demonstrates good performances. The FF-BSS systems and the FB-BSS system are compared based on the transmission time difference in the mixing process. Location of the signal sources and the sensors are rather limited in the FB-BSS system. © 2006 IEEE.
続きを見る
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| 12.
論文 |
論文
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Nakayama, Kenji ; Horita, Akihide ; Hirano, Akihiro
概要:
金沢大学理工研究域 電子情報学系<br />Feed-Forward (FF-) and Feed-Back (FB-) structures have been proposed for Blind Source Separation (
…
BSS). The FF-BSS systems have some degrees of freedom in the solution space, and signal distortion is likely to occur in convolutive mixtures. On the other hand, the FB-BSS structure does not cause signal distortion. However, it requires a condition on the propagation delays in the mixing process. In this paper, source separation performance in the FB-BSS is theoretically analyzed taking the propagation delays into account. Simulation is carried out by using white signals and speech signals as the signal sources. The FF-BSS system and the FB-BSS system are compared. Even though the FB-BSS can provide good separation performance, there exits some limitation on location of the signal sources and the sensors. © Springer-Verlag Berlin Heidelberg 2007.
続きを見る
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| 13.
論文 |
論文
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Horita, Akihide ; Nakayama, Kenji ; Hirano, Akihiro ; Dejima, Yasuhiro
概要:
金沢大学理工研究域 電子情報学系<br />Source separation and signal distortion are theoretically analyzed for the FF-BSS systems implemen
…
ted in both the time and frequency domains and the FB-BSS system. The FF-BSS systems have some degree of freedom, and cause some signal distortion. The FB-BSS has a unique solution for complete separation and distortion free. Next, the condition for complete separation and distortion free is derived for the FF-BSS systems. This condition is applied to the learning algorithms. Computer simulations by using speech signals and stationary colored signals are carried out for the conventional methods and the new learning algorithms employing the proposed distortion free constraint. The proposed method can drastically suppress signal distortion, while maintaining high separation performance. The FB-BSS system also demonstrates good performances. The FF-BSS systems and the FB-BSS system are compared based on the transmission time difference in the mixing process. Location of the signal sources and the sensors are rather limited in the FB-BSS system. © 2006 IEEE.
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| 14.
論文 |
論文
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Nakayama, Kenji ; Horita, Akihide ; Hirano, Akihiro
概要:
金沢大学理工研究域 電子情報学系
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| 15.
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論文
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Horita, Akihide ; Nakayama, Kenji ; Hirano, Akihiro ; Dejima, Yasuhiro
概要:
金沢大学理工研究域 電子情報学系<br />畳み込み混合過程におけるフィードフォワード(FF-)形ブラインドソースセパレーション(BSS)では自由度が存在するため信号歪みが生じる.我々は,信号源-センサーが2チャンネルの場合において,完全分
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離と信号無歪みの条件を制約条件として課す信号歪み抑制学習アルゴリズムを時間領域,周波数領域のFF-BSSに対して提案してきた.本稿では,信号歪み抑制の制約条件を多チャンネルに拡張し,かつ,計算の複雑さを軽減するために制約条件を近似する方式を提案する.音声を用いたコンピュータシミュレーションによってその近似制約方式と厳密制約方式がほぼ同等の分離性能と信号歪み抑制が得られることを確認した.また,3チャンネルにおいても,従来方式より特性が改善されることを確認した. Feed-forward Blind Source Separation (FF-BSS) systems have some degree of freedom in the solution space, and signal distortion is likely to occur in convolutive mixtures. Previously, a condition for complete separation and distortion free has been derived for 2-channel FF-BSS. This condition has been applied to the learning algorithms as a distortion free constraint in both the time and frequency domains. In this paper, the condition is further extended to multiple channel FF-BSSs. This condition requires the a high computational complexity to be applied to the learning process as a constraint. An approximate constraint is proposed in order to relax the high computational load. In comparison with the original constraint, computer simulations have demonstrated that the approximation can obtain similar performances with respect to source separation as well as signal distortion using speech signals. Furthermore, the performances can be improved compared to the conventionals for three channels.
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