Before

 

 

 

 

 

 

 

 

 

After

 

 

 

 

 

 

 

 

This recording was made from a listening device hidden under a lounge. The major problem with this file is loud hiss. As mentioned in the previous demo, the first thing I did with this file is convert it into a lossless format (wav) for further work. Next I ran the file in the Diamond Cut Forensics 30 band graphics equalizer, using the default speech setting, cutting out any audio that isn't in the vocal frequency range.

 

Next, I continued with Diamond Cut and ran the Auto Voice Filter. The Auto Voice Filter is an adaptive system optimized for separating voice signals from random noise automatically. It uses two independent mathematical processes that work in harmony with one another in order to perform its job.  One process identifies statistical noise while the other process identifies the human voice component of a signal.

 

Finally, again in Diamond Cut I ran the Spectral filter, using the Default Forensics setting. The Spectral Filter is designed to remove stationary or slowly changing tonal noise and broadband hiss by learning a profile of the offending noise and then subtracting it from the signal.

 

That was the final edit I needed to make for this audio file.

Before

 

 

 

 

 

 

 

After

 

 

 

 

 

 

 

 

The major problem with this file is loud hiss, as with the previous example, so I followed the exact same process. As mentioned in he previous demo, the first thing I did with this file is convert it into a lossless format (wav) for further work. Next I ran the file in the Diamond Cut Forensics 30 band graphics equalizer, using the default speech setting, cutting out any audio that isn't in the vocal frequency range.

 

Next, I continued with Diamond Cut and ran the Auto Voice Filter. The Auto Voice Filter is an adaptive system optimized for separating voice signals from random noise automatically. It uses two independent mathematical processes that work in harmony with one another in order to perform its job.  One process identifies statistical noise while the other process identifies the human voice component of a signal.

 

Finally, again in Diamond Cut I ran the Spectral filter, using the Default Forensics setting. The Spectral Filter is designed to remove stationary or slowly changing tonal noise and broadband hiss by learning a profile of the offending noise and then subtracting it from the signal.

 

That was the final edit I needed to make for this audio file.

Before

 

 

 

 

 

 

 

 

After

 

 

 

 

 

 

 

 

The major problem with this file is loud hiss, as with the previous example, so I followed the exact same process. As mentioned in he previous demo, the first thing I did with this file is convert it into a lossless format (wav) for further work. Next I ran the file in the Diamond Cut Forensics 30 band graphics equalizer, using the default speech setting, cutting out any audio that isn't in the vocal frequency range.

 

Next, I continued with Diamond Cut and ran the Auto Voice Filter. The Auto Voice Filter is an adaptive system optimized for separating voice signals from random noise automatically. It uses two independent mathematical processes that work in harmony with one another in order to perform its job.  One process identifies statistical noise while the other process identifies the human voice component of a signal.

 

Finally, again in Diamond Cut I ran the Spectral filter, using the Default Forensics setting. The Spectral Filter is designed to remove stationary or slowly changing tonal noise and broadband hiss by learning a profile of the offending noise and then subtracting it from the signal.

 

That was the final edit I needed to make for this audio file.

 

 

 

 

 

 

 

 

 

 

Before

 

 

 

 

 

 

 

After

 

 

 

 

 

 

 

 

 

The major problem with this file is loud hiss, as with the previous example, so I followed the exact same process. As mentioned in he previous demo, the first thing I did with this file is convert it into a lossless format (wav) for further work. Next I ran the file in the Diamond Cut Forensics 30 band graphics equalizer, using the default speech setting, cutting out any audio that isn't in the vocal frequency range.

 

Next, I continued with Diamond Cut and ran the Auto Voice Filter. The Auto Voice Filter is an adaptive system optimized for separating voice signals from random noise automatically. It uses two independent mathematical processes that work in harmony with one another in order to perform its job.  One process identifies statistical noise while the other process identifies the human voice component of a signal.

 

Finally, again in Diamond Cut I ran the Spectral filter, using the Default Forensics setting. The Spectral Filter is designed to remove stationary or slowly changing tonal noise and broadband hiss by learning a profile of the offending noise and then subtracting it from the signal.

 

That was the final edit I needed to make for this audio file.

 

 

 

 

 

 

 

 

 

 

Before

 

 

 

 

 

 

 

After

 

 

 

 

 

 

 

 

The major problem with this file is loud hiss, as with the previous example, so I followed the exact same process. As mentioned in he previous demo, the first thing I did with this file is convert it into a lossless format (wav) for further work. Next I ran the file in the Diamond Cut Forensics 30 band graphics equalizer, using the default speech setting, cutting out any audio that isn't in the vocal frequency range.

 

Next, I continued with Diamond Cut and ran the Auto Voice Filter. The Auto Voice Filter is an adaptive system optimized for separating voice signals from random noise automatically. It uses two independent mathematical processes that work in harmony with one another in order to perform its job.  One process identifies statistical noise while the other process identifies the human voice component of a signal.

 

Finally, again in Diamond Cut I ran the Spectral filter, using the Default Forensics setting. The Spectral Filter is designed to remove stationary or slowly changing tonal noise and broadband hiss by learning a profile of the offending noise and then subtracting it from the signal.

 

That was the final edit I needed to make for this audio file.

Before

 

 

 

 

 

 

 

 

After

 

 

 

 

 

 

 

 

 

The major problem with this file is loud hiss, as with the previous example, so I followed the exact same process. As mentioned in he previous demo, the first thing I did with this file is convert it into a lossless format (wav) for further work. Next I ran the file in the Diamond Cut Forensics 30 band graphics equalizer, using the default speech setting, cutting out any audio that isn't in the vocal frequency range.

 

Next, I continued with Diamond Cut and ran the Auto Voice Filter. The Auto Voice Filter is an adaptive system optimized for separating voice signals from random noise automatically. It uses two independent mathematical processes that work in harmony with one another in order to perform its job.  One process identifies statistical noise while the other process identifies the human voice component of a signal.

 

Finally, again in Diamond Cut I ran the Spectral filter, using the Default Forensics setting. The Spectral Filter is designed to remove stationary or slowly changing tonal noise and broadband hiss by learning a profile of the offending noise and then subtracting it from the signal.

 

That was the final edit I needed to make for this audio file.

 

 

 

 

Before

 

 

 

 

 

 

 

After

 

 

 

 

 

 

 

 

The major problem with this file is loud hiss, as with the previous example, so I followed the exact same process. As mentioned in he previous demo, the first thing I did with this file is convert it into a lossless format (wav) for further work. Next I ran the file in the Diamond Cut Forensics 30 band graphics equalizer, using the default speech setting, cutting out any audio that isn't in the vocal frequency range.

 

Next, I continued with Diamond Cut and ran the Auto Voice Filter. The Auto Voice Filter is an adaptive system optimized for separating voice signals from random noise automatically. It uses two independent mathematical processes that work in harmony with one another in order to perform its job.  One process identifies statistical noise while the other process identifies the human voice component of a signal.

 

Finally, again in Diamond Cut I ran the Spectral filter, using the Default Forensics setting. The Spectral Filter is designed to remove stationary or slowly changing tonal noise and broadband hiss by learning a profile of the offending noise and then subtracting it from the signal.

 

That was the final edit I needed to make for this audio file.